Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...acijjournal
Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph
clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness
and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will
provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and
MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering
technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by
assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A.
D. King only for undirected and unweighted random graphs. Another popular graph clustering
algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful
applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e.
nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of
human sexual contacts, or the chemical network of a cell etc., are basically following power-law
distribution to represent different real-life systems. This paper uses real large-scale power-law
distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov
clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law
distribution datasets reveal the effectiveness of our approach to comparative performance measure of
these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and
normalized mutual information (NMI).
System for Prediction of Non Stationary Time Series based on the Wavelet Radi...IJECEIAES
This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...acijjournal
Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph
clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness
and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will
provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and
MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering
technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by
assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A.
D. King only for undirected and unweighted random graphs. Another popular graph clustering
algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful
applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e.
nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of
human sexual contacts, or the chemical network of a cell etc., are basically following power-law
distribution to represent different real-life systems. This paper uses real large-scale power-law
distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov
clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law
distribution datasets reveal the effectiveness of our approach to comparative performance measure of
these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and
normalized mutual information (NMI).
System for Prediction of Non Stationary Time Series based on the Wavelet Radi...IJECEIAES
This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
On the High Dimentional Information Processing in Quaternionic Domain and its...IJAAS Team
There are various high dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. The conventional real-valued neural networks are tried to solve the problem associated with high dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a well generalized learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is presented through a wide spectrum of simulations which demonstrate the significance of the work.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Jurnal Hadri Kusuma Tentang Ukuran PerusahaanTrisnadi Wijaya
Jurnal ini berisikan teori-teori Ukuran Perusahaan (Firm Size). Anda dapat memanfaatkan Kajian Teoritis mengenai Ukuran Perusahaan yang ada pada artikel ini.
T AXONOMY OF O PTIMIZATION A PPROACHES OF R ESOURCE B ROKERS IN D ATA G RIDSijcsit
A novel taxonomy of replica selection techniques is proposed. We studied some data grid approaches
where the selection strategies of data management is different. The aim of the study is to determine the
common concepts and observe their performance and to compare their performance with our strategy
On the High Dimentional Information Processing in Quaternionic Domain and its...IJAAS Team
There are various high dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. The conventional real-valued neural networks are tried to solve the problem associated with high dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a well generalized learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is presented through a wide spectrum of simulations which demonstrate the significance of the work.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Jurnal Hadri Kusuma Tentang Ukuran PerusahaanTrisnadi Wijaya
Jurnal ini berisikan teori-teori Ukuran Perusahaan (Firm Size). Anda dapat memanfaatkan Kajian Teoritis mengenai Ukuran Perusahaan yang ada pada artikel ini.
T AXONOMY OF O PTIMIZATION A PPROACHES OF R ESOURCE B ROKERS IN D ATA G RIDSijcsit
A novel taxonomy of replica selection techniques is proposed. We studied some data grid approaches
where the selection strategies of data management is different. The aim of the study is to determine the
common concepts and observe their performance and to compare their performance with our strategy
Clustering of Deep WebPages: A Comparative Studyijcsit
The internethas massive amount of information. This information is stored in the form of zillions of
webpages. The information that can be retrieved by search engines is huge, and this information constitutes
the ‘surface web’.But the remaining information, which is not indexed by search engines – the ‘deep web’,
is much bigger in size than the ‘surface web’, and remains unexploited yet.
Several machine learning techniques have been commonly employed to access deep web content. Under
machine learning, topic models provide a simple way to analyze large volumes of unlabeled text. A ‘topic’is
a cluster of words that frequently occur together and topic models can connect words with similar
meanings and distinguish between words with multiple meanings. In this paper, we cluster deep web
databases employing several methods, and then perform a comparative study. In the first method, we apply
Latent Semantic Analysis (LSA) over the dataset. In the second method, we use a generative probabilistic
model called Latent Dirichlet Allocation(LDA) for modeling content representative of deep web
databases.Both these techniques are implemented after preprocessing the set of web pages to extract page
contents and form contents.Further, we propose another version of Latent Dirichlet Allocation (LDA) to the
dataset. Experimental results show that the proposed method outperforms the existing clustering methods.
A rule based approach towards detecting human temperamentijcsit
This paper presented a rule based system for detecting human temperament.. The system was developed to
provide support for an expert psychologist in properly predicting the temperament of an individual as well
as given advice to the user. The system does this by following specified rules. Of this, we have deduced
some features that makes up known temperament types from which the system can accurately classify the
user‘s temperament based on the person‘s characters. Also, our work is solely limited to temperament, any
expert advice sought from and given by the system is limited to this scope.
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
P REDICTION F OR S HORT -T ERM T RAFFIC F LOW B ASED O N O PTIMIZED W...ijcsit
Short term traffic forecasting has been a very impo
rtant consideration in many areas of transportation
research for more than 3 decades. Short-term traffi
c forecasting based on data driven methods is one o
f the
most dynamic and developing research arenas with en
ormous published literature. In order to improve
forecasting model accuracy of wavelet neural networ
k, an adaptive particle swarm optimization algorith
m
based on cloud theory was proposed, not only to hel
p improve search performance, but also speed up
individual optimizing ability. And the inertia weig
ht adaptively changes depending on X-conditional cl
oud
generator which has the stable tendency and randomn
ess property .Then the adaptive particle swarm
optimization algorithm based on cloud theory was us
ed to optimize the weights and thresholds of wavele
t
BP neural network, Instead of traditional gradient
descent method . At last, wavelet BP neural network
was
trained to search for the optimal solution. Based o
n above theory, an improved wavelet neural network
model based on modified particle swarm optimization
algorithm was proposed and the availability of the
modified prediction method was proved by predicting
the time series of real traffic flow. At last, the
computer simulations have shown that the nonlinear
fitting and accuracy of the modified prediction
methods are better than other prediction methods.
Multimode system condition monitoring using sparsity reconstruction for quali...IJECEIAES
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The Internet paved way for information sharing all over the world decades ago and its popularity for distribution of data has spread like a wildfire ever since. Data in the form of images, sounds, animations and videos is gaining users’ preference in comparison to plain text all across the globe. Despite unprecedented progress in the fields of data storage, computing speed and data transmission speed, the demands of available data and its size (due to the increase in both, quality and quantity) continue to overpower the supply of resources. One of the reasons for this may be how the uncompressed data is compressed in order to send it across the network. This paper compares the two most widely used training algorithms for multilayer perceptron (MLP) image compression – the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient algorithm. We test the performance of the two training algorithms by compressing the standard test image (Lena or Lenna) in terms of accuracy and speed. Based on our results, we conclude that both algorithms were comparable in terms of speed and accuracy. However, the Levenberg- Marquardt algorithm has shown slightly better performance in terms of accuracy (as found in the average training accuracy and mean squared error), whereas the Scaled Conjugate Gradient algorithm faired better in terms of speed (as found in the average training iteration) on a simple MLP structure (2 hidden layers).
With the increase in Internet users the number of malicious users are also growing day-by-day posing a
serious problem in distinguishing between normal and abnormal behavior of users in the network. This
has led to the research area of intrusion detection which essentially analyzes the network traffic and tries
to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard
NSL-KDD intrusion dataset using some neural network based techniques for predicting possible
intrusions. Four most effective classification methods, namely, Radial Basis Function Network, Self-
Organizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been
applied. In order to enhance the performance of the classifiers, three entropy based feature selection
methods have been applied as preprocessing of data. Performances of different combinations of classifiers
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NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
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architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
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COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRISE FINANCIAL DISTRESS
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
DOI:10.5121/ijcsit.2015.7307 83
COMPARISON OF WAVELET NETWORK AND
LOGISTIC REGRESSION IN PREDICTING
ENTERPRISE FINANCIAL DISTRESS
Ming-Chang Lee1
and Li-Er Su2
1
National Kaohsiung University of Applied Sciences
2
Shih Chien University, Kaohsiung Campus, Kaohsiung Taiwan
ABSTRACT
Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate
performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks in
non-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.
ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.
Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. It
discussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rate
and error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity
exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on
training and forecasting sample, the result shows that this wavelet network model is high accurate and the
overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better than
logistic regression model.
KEYWORDS
Wavelet Networks; Logistic Regression; Business Failure Prediction; Type I error; Type Ⅱerror.
1. INTRODUCTION
Business Failure Prediction (BFP) can help to avoid investing in business likely to fail.
Bankruptcy prediction model enhance the decision support tool and improve decision making
[33]. It uses statistical analysis and data mining technique. Statistical business failure prediction
models attempt to predict the business’ failure or success. The Multiple discriminant analysis
(MDA) has been the most popular approaches [6, 13]. There have a large number of alternative
techniques available in this MAD model [2, 9, 22, 33]. The data mining techniques include
decision tree, support vector machine (SVM) [10], neural networks (NNs) [11], fuzzy system,
rough set theory and genetic algorithm (GA) [33]. Various researches have demonstrated the
artificial intelligence (AI) techniques serve as a useful tool bankruptcy prediction such as artificial
neural networks (ANNs) [30].
The following method or model using for prediction in time series are Box-Jenkins method [28],
Grey forecasting model [11], artificial neural networks model [1, 4, 16], Logistic Regression [25,
30], ARMA Model [21]. In Box-Jenkins method, it is difficult to determine the model features;
it is also difficult to identify no-steady state. It is not need to calculate the statistical characters in
grey forecasting model or neural network model. But, grey forecasting model is suitable for
solve exponential growth practical problems. The network modelling approach in artificial neural
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
84
networks model is difficult to determine the network structure by using scientific application. In
training and learning state, BP model calculate the optimal weight. This model trapped in a local
minimum which will affect the reliability and accuracy of the model. ARMA Model provides
one of the basic tools in time series modelling. This method is valid in pure AR model and pure
MA model. But, it is difficult for identification in mixed ARMA model. Therefore, using
wavelet network clustering capabilities, the input time series sample autocorrelation function
(SACF) value and partial autocorrelation function (SPACF) value, the output is the identification
of ARMA model. Many researchers [20, 29, 36] discussed the ability of nonlinear approximation
of wavelets and neural network models. Some researches [31, 32, 34] studied fuzzy wavelet
neural network models for prediction and identification of dynamical systems.
The main contribution of this paper is to propose a financial distress prediction method based on
Wavelet Network model and logistic regression model. The financial and non-financial ratios
were used to enhance the accuracy of the financial distress prediction model. The dimension of
the inputs was reduced using a principal component analysis (PCA) method, and then Wavelet
Network model were used to predict the financial distress.
2. LOGISTIC REGRESSION MODEL AND WAVELET NETWORK
MODEL
2.1. Logistic regression model
Defines the probability )(xπ and 1- )(xπ , these probabilities are written in the following form:
),...,,1()( 21 nXXXYPx ==π
),...,,0()(1 21 nXXXYPx ==−π (1)
This model of the ln
)(1
)(
x
x
π
π
−
is:
∑+=
−
=
=−
=
=
n
j
jj
n
n
X
x
x
XXXYP
XXXYP
1
0
21
21
)(1
)(
ln
),...,,1(1
),...,,1(
ln ββ
π
π
(2)
Using the inverse of the Logit transformation of (2), it obtains at the following:
∑
=
+−∑
=
+
∑
=
+
+
=
+
== n
j
jXj
n
j
jXj
n
j
jXj
n
ee
e
XXXYP
1
)0(
1
0
1
0
21
1
1
1
),...,,1(
ββββ
ββ
(3)
(3) is a logistic regression model, the conditional mean is between 0 and 1.
2.2. Maximum Likelihood Estimate (MLE)
The maximum likelihood is the method of parameter estimation in logistic regression model. For
a set of observations in the data ),( ii yx , the following equation results for the contribution to the
likelihood function for the observation ),( ii yx is )( ixα :
]1)(1[)()( iy
ixiy
ixix −−= ππα (4)
The observations are assumed to be independent of each other so it can multiply their likelihood
contributions to obtain the complete likelihood function )(βl . The result is given in (5).
Where β is the collection of parameters ( nβββ ,...,, 10 ) and )(βl is the likelihood function of β .
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
85
∏
=
=
n
i
ixl
1
)()( αβ (5)
)(BL is denoted the log likelihood expression.
)])(1ln[)
1
1()](ln[()](ln[)( ixiy
n
i
ixiylL ππββ −∑
=
−+== (6)
It employs the techniques of calculus to determine the value of β based on maximum of )(βL . It
takes the derivate of )(βL with respect to nβββ ,...,, 10 and setting the resulting derivatives equal
to zero. These equations are called likelihood estimations, and there is n +1 equation.
2.3. Hypothesis testing
The hypothesis testing that a coefficient on an independent variable is significantly different from
zero use Wald statistic. The Wald statistic for the β coefficient is:
=2χ Wald = 2]/[ βσβ (7)
This is distributed chi-square with 1 degree of freedom.
2.4. Wavelet network model t
Wavelet analysis is a new technique designed for multi scale analysis in the time series
modelling. Wavelet analysis utilizes the wavelet basis function to approximate and extract the
features of interests from the original data. If the function )(tϕ satisfies the following condition
is called wavelet basis function.
∫
∝ <∝0
)(
dw
w
wϕ
(8)
Where )(wϕ is the Fourier transform of )(tϕ in the frequency domain. Variable t can actually
have other unit [12].
The wavelet basis function )(
1
)(,
a
bt
a
tba
−
= ϕϕ (9)
Where ‘a’ refers the scale (dilated) coefficient, ‘b’ refers the translation coefficient.
Wavelet networks employ activation function that are dilated and translated coefficient of a single
function RdR →:ϕ , where d is the input dimension [36]. This function called the ‘mother
wavelet’ is localized both in the space and frequency domains [7]. In order to increase
convergence speed, the wavelet neural network shows surprising effectiveness in solving the
conventional problem of poor convergence or even divergence encountered in other kind of
neural network [38].
Wavelet network model is feed forward network model; the wavelet basis function is neuronal
activation function. The basic strategy is changing the shape and scale wavelet basis, adjusting
the network weights and threshold value to take the error function minimization. Figure 1 is
Wavelet neural network structure.
Three layers of the wavelet neural network are input layer, hidden layer and output layer. Each
layer is fully connected to the nodes in the next layer.
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
86
}{ ix : The Input value of the sample, Mi ,..,2,1= .
}{ py : The output value of the sample p.
kw : The weight of linking hidden layer with output layer, Kk ,..,2,1=
The output value of the sample p in network:
]
1 1
)([ ∑
=
∑
=
−
=
K
k
M
i ka
kbi
ixkwpy ϕσ : ))exp(1(1)( tt −+=σ (10)
<
>
=
5.0)(0
5.0)(1
t
t
y
σ
σ
x1 ○ )
1
12
(
a
b−
ϕ
x2 ○ )
1
1(
a
bM −
ϕ w1
y
)
1
(
ka
kb−
ϕ wk
xM ○ )
2
(
ka
kb−
ϕ
)(
ka
kbM −
ϕ
Figure 1: Wavelet neural network structure
The minimum mean error function:
∑
=
−=
P
p
pypdE
1
2){
2
1
(11)
Where
:pd The output of expected classification of the sample p, :pd = 1 or 0
M: The number of input layer.
K: The number of hidden layer
P: The number of sample
2.5. Wavelet network model training algorithm
The training algorithm for a wavelet neural network is as follows [35].
(1) Select the no of hidden nodes required. Initialize parameters kwandkbka ,, with random
number in ].1,1[− Give objective mean error maxE . The Mother wavelet is has the following
form
)22exp()75.1cos()( ttt −=ϕ (12)
]22exp()]75.1sin(75.1)75.1cos([ tttt
dt
d
−+−=
ϕ
)
1
11
(
a
b−
ϕ
Σ
Σ
σ
Σ
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
87
When taken as Gaussian wavelet it become
)22exp()( tt −=ϕ (13)
(2) Input training sample }{ ix and the objective output }{ pd . Calculate the output }{ py and he
minimum mean error function E.
(3) Adjust kwandkbka ,, for reducing the error of production. Use the conjugate gradient
descend algorithm is employed:
)()()1( tkw
kw
E
tkwtkw ∆+
∂
∂
+=+ αη
)()()1( tka
ka
E
tkatka ∆+
∂
∂
+=+ αη (14)
)()()1( tkb
kb
E
tkbtkb ∆+
∂
∂
+=+ αη
ka
kbi
t
−
='
Where
∑
=
∑
=
−−=
∂
∂ P
p
M
i
tixtpypd
kw
E
1 1
)'()'(')( ϕσ
=
∂
∂
ka
E
)'
1 1
()'()( t
P
p
M
i ka
t
ka
ixkwpypd∑
=
∑
= ∂
∂
∂
∂
−− σϕ (15)
−=
∂
∂
kb
E
)'(
1 1
)'()( t
P
p
M
k kb
t
kb
kw
p
ky
p
kd σϕ∑
=
∑
= ∂
∂
∂
∂
−
Where η and α are the learning and the momentum rates respectively.
(4) Return to step (2) the process is continued until E satisfies the give error criteria, and the
whole training of wavelet neural network is completed.
2.6. Accuracy rate and error rate
Since Type I error only creates a lost opportunity cost from not dealing with a successful
business, for example, missed potential investment gains [17], therefore Type I error is more
important than Type II error. The objectives of predictive of accuracy should be to reduced Type I
error while keep Type II error. Table 1 denotes as robustness of model. The measure used for
calculating the accuracy of classifying distressed companies and no-distressed companies have:
Type I error =
BA
B
+
Type II error =
DC
C
+
E )/( BAA +=
F )/( DCD +=
G )/()( DCBADA ++++=
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
88
Where
Type I error: refer to the situation when actual failure company is classified as non
failures company
Type II error: refer to the situation when actual non failure company is classified as
Failures Company.
C: the number of Type I error that is the number of distressed companies in the sample
based on actual observation that was misclassified as a non-distressed company.
B: the number of Type II error that is the number of non-distressed companies in the
sample based on actual observation that was misclassified as a distressed company.
A: the number of non-distressed accurately classified by the models
D: the number of distressed accurately classified by the models
E: The accuracy of classification of non-distressed company
F: The accuracy of classification of distressed company
G: The oval accuracy of classification
Table 1: Robustness of model
No-distressed
Observed value
Non-distressed
company
Distressed company Accuracy of
Classification
Non-distressed company A B E
Distressed company C D F
Overall accuracy of
classification
G
3. EMPRICAL RESEARCH
We random select in the stock market listed company's traditional manufacturing in Taiwan
to analysis the wavelet network model prediction. The steps of business failure prediction model
(Wavelet network model and logistic regression model) are:
Step 1: Financial ratio of dataset
Step 2: Identification of independent variables
Step 3: Reduced the number of financial ratio
(a) Kolmogorov-Smirnov test (K-S test)
(b) Wilcoxon test
(c) Principal component analysis
Step 4: Building the wavelet networks model
(a) Wavelet networks model
(b) Training for a wavelet neural network
(c) Robustness of model in prediction accuracy
Step5: Building Logistic regression model
(a) Logistic regression model
(b) Training for Logistic regression
(c) Robustness of model in prediction accuracy
Step 6: Comparison of Wavelet Network and Logistic Regression
3.1. Financial Ratio of Dataset
A dataset of covariates used in this study includes a combination of financial ratios and market
variables [15]. Some researches such as [3, 8, 9, 23~24, 26~27, 39] have been widely used
financial ratios in explaining the possibility of business financial distress. Gibson [14] suggests
five factors (Profitability, Liquidity, Leverage, Efficiency, and Valuation ratio) for evaluation enterprise
financial failure. Table 3 is the 15 rations selected in this study.
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
89
Table 3: The 15 rations selected in this study
Category Covariate Code Definition
x1 Profitability EBIT margin EBT EBIT/operating revenue
x2 Return on Equity ROE Net income/ Total equity
x3 Return on Assets ROA Net income/ Total assets
x4 Liquidity Current ratio CUR Current assets/ current liabilities
x5 Quick ration QUK Quick assets/ current liabilities
x6 Leverage Debt ratio DET Total liabilities/ Total assets
x7 Debt to Equity ratio DER Total liabilities/ Total equity
x8 Efficiency Fixed Asset turnover FAT Revenue/Asset
x9 Capital turnover CAT Operating revenue/ operating invest
capital
x10
Valuation ratio
Price to Sales ratio PSR Stock price per share/ Sales per share
x11 Price earnings ratio PER Stock price per share/Earnings per
share
x12 Price to book value PBV Stock price per share/Equity per
share
x13 Growth ability Operating Profit grew OPG Operating Profit grew rate
x14 Net profit grow rate NPG Net profit grow rate
x15 Inventory turning rate ITR Sale/ Inventory
3.2. Data collection and Sample
This paper random select sample listed companies from 2006 to 2009 for estimating sample.
3.3. Reduced the number of financial ratio
There are three ways to reduce the large number of financial indicators (1) Financial indicators
normal distribution test (Kolmogorov-Smirnov test (K-S test)) (2) Paired samples’ significant test
in financial indicators (Wilcoxon test) (3) Principal component analysis (PCA). PCA is a
statistical procedure that uses an orthogonal transformation to convert a set of observations of
possibly correlated variables into a set of values of linearly uncorrelated variables called principal
components. The number of principal components is less than or equal to the number of original
variables.
(1) Kolmogorov-Smirnov test (K-S test)
The K-S test has the advantage of making no assumption about the distribution of data. Suppose
that an Independent and Identically Distributed (i.i.d.) sample nxxx ,...2,1 with some unknown
distribution p and we would like to test the hypothesis that p is equal to a particular (normal)
distribution 0
p , i.e. decide between the following hypotheses:
0
H : 0
pp = , :1H 0
pp ≠ . We know that testing this hypothesis use chi-squared goodness-of-
fit test.
(2) Wilcoxon test (Wilcoxon signed-rank test)
The Wilcoxon signed-rank test is a non-parametric statistical hypothesis test used when
comparing two related samples, matched samples, or repeated measurements on a single sample
to assess whether their population mean ranks differ (i.e. it is a paired difference test). Wilcoxon
test used as (1) an alternative to the paired Student's t-test, (2) t-test for matched pairs, (3) the t-
test for dependent samples when the population cannot be assumed to be normally distributed.
(3) Principal component analysis (PCA)
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
90
The main purposes of a PCA are (1) the analysis of data to identify patterns (2) finding patterns to
reduce the dimensions of the dataset with minimal loss of information. In the table of principal
component variance, it select no of components in this data testing, according to the cumulative
variance above to 95%.
3.4. Building the wavelet networks model
In wavelet network model, the numbers of neurons of the input and output layers are respectively
equal to the number of variables presented simultaneously to the network and to the number of
variables that the neural network must estimate. Wavelet network model the number of principal
components is equal to the number of input layer neuron. The number of output layer neuron is
1. The output neuron takes the value 0 if the company is healthy (no-financial distress) and 1 if it
is bankrupt (financial distress). To determine the number of hidden layer node is by researcher.
After determining the network architecture, its implementation requires two data samples. The
first set of data is used for the training, the second for the validation. The neural network is been
realized using Matlab software. Model input values of paired samples are the score value of
principal components.
3.5 Building Logistic regression model
Logistic regression model input values of paired samples are the score value of principal
components. The dependent variable in logistic regression is usually dichotomous, that is, the
dependent variable can take the value 1 with a probability of success )( iyp , or the value 0 with
probability of failure )(1 iyp− .
The Logistic regression is been realized using Spss21.0 software and the Wald statistic for test the
βˆ coefficient.
The predict probability of occurrence )( iyp given as:
)]}ˆ....11
ˆ
0
ˆ(exp[1{1)( mxmxiyp βββ +++−+=
The output 5.0)( >iyp takes the value 0, the company is healthy (no-financial distress) and
5.0)( <iyp takes the value 1, and the company is bankrupt.
3.6. Robustness of model in prediction accuracy
It easily calculates the accuracy rate and Error rate. Type I error is
BA
B
+
% and Type II error is
DC
C
+
% (See Table 1).
4. EMPIRICAL ANALYSIS
4.1. Data collection and Sample
This paper select sample listed companies from 2006 to 2009 for training sample. There select 40
paired samples for analysis. It randomly selected 20 paired sample listed companies from 2009 to
2012 for forecasting sample.
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
91
Through the kolmogorov- Siminor test (using SPSS20.0 software), no financial indicator on
paired sample is normal distribution. Through wilcoxon test, ten variables are selected as
potential predictor variables: EBIT margin (x1), Return on Equity (x2), Return on Assets (x3),
Current ratio (x4), Quick ration (x5), Fixed Asset turnover (x8), Price earnings ratio (x11), Price to
book value (x12), Operating Profit grew (x13), Net profit grow rate (x14). The paired financial
indicators sample wilcoxon test denotes as Table 4.
Table 4: The paired financial indicators sample wilcoxon test
Financial
indicator
Statistic Z-
value
2-taied test p-value Result
X1 (EBIT) -2.012 0.042 *
X2 (ROE) -3.325 0.001 *
X3 (ROA) -4. 855 0.000 *
X4 (CUR) -4.121 0.000 *
X5 (QUR) -1.968 0.045 *
X6 (DET) -1.763 0.084
X7 (DER) -0.213 0.812
X8 (FAT) -2.164 0.032 *
X9 (CAT) -1.357 0.160
X10 (PSR) -1. 656 0.085
X11 (PER) -3.902 0.002 *
X12 (PBV) -1.993 0.047 *
X13 (OPG) -2.133 0.034 *
X14 (NPG) -3.258 0.001 *
X15 (ITR) -1.885 0.068
* Significant at 5 percent level
4.2. Principal component analyse
PCA is statistical procedure that uses an orthogonal transformation to convert a set of
observations of possibly correlated variables into a set of values of linearly uncorrelated variables.
It uses SPSS 21.0 software on PCA. It gets 8 principal components (z1, z2, z3, z4, z5, z6, z7, z8), it
contains the original 10 financial indicators 97.82% (see table 5).
Tab 5: The principal component Variance
Principal
components
Eigenvalue Variance (%) Cumulated Variance
(%)
z1 3.165 31.642 31.642
z2 1.315 13.043 44.685
z3 1.088 10.863 55.548
z4 0.935 9.347 64.895
z5 0.921 9.225 74.120
z6 0.834 8.336 82.456
z7 0.781 7.712 90.168
z8 0.753 7.652 97.820
Z9 0.155 1.540 99.760
Z10 0.065 0.640 100.00
The component score express as:
*
1414
*
1313
*
1212
*
1111
*
88
*
55
*
44
*
33
*
22
*
11 xiaxiaxiaxiaxiaxiaxiaxiaxiaxiaiz +++++++++=
10. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
92
Where ,8....,,2,1=i
is
ixix
x
−
=*
1 , :ix the mean of financial indicator, :is the standard
deviation of financial indicator. Table 6 is denoted as the component scored matrix.
For example,
*
14176.0*
13085.0*
12052.0*
11034.0*
8042.0*
5056.0*
4615.0*
3538.0*
203.0*
1068.01 xxxxxxxxxxz −−−−−−++−−=
Table 6: The component scored matrix.
Principal
component
1z 2z 3z 4z 5z 6z 7z 8z
*
1x (EBIT)
-0.068 0.008 0.054 -0.013 0.046 0.017 0.072 0.027
*
2x (ROE)
-0.03 -0.011 -0.112 -0.042 -0.006 0.003 -
0.003-
-0.041
*
3x (ROA)
0.538 -0.036 -0.035 -0.058 -0.024 -0.036 -0.012 -0.124
*
4x (CUR)
0.615 -0.010 -0.042 -0.042 -0.038 -0.035 -0.003 -0.127
*
5x (QUR)
-0.056 0.040 0.019 0.016 0.011 0. 986 0.006 -0.011
*
8x (FAT)
-0.042 0.032 0.012 -0.043 0.0031 0.036 0.052 0.053
*
11x (PER)
-0.034 0.056 0.079 0.009 -0.006 0.002 0.912 -0.019
*
12x (PBV)
-0.052 0.031 0.048 -0.007 0.589 0.017 -0.054 -0.078
*
13x (OPG)
-0.085 -0. 042 -0.012 0.831 -0.006 0. 015 0.008 -00.94
*
14x (NPG)
-0.176 0.055 0.024 -0.017 -0.068 -0.011 -0.017 5.015
4.3. Wavelet network model training and forecasting
The wavelet network training and forecasting use Mat lab software. Vector
)8,7,6,5,4,3,1,1( zzzzzzzz is input of this model. Model input values of paired samples are 40,
the input layer neuron is 8. The hidden layer neuron is 6 and the output layer neuron is 1. Set
minimum mean error function 001.0=E , 1.0=η , 01.0=α . The paired samples are 40 for training
test. Predicted results denoted as table 7.
Table 7: Robustness of model (training sample)
No-distressed
Observed value
Non-distressed
company
Distressed company Accuracy of
Classification (%)
Non-distressed company 38 2 95
Distressed company 1 39 97.5
Overall accuracy of
classification
96.25
Type I error is
BA
B
+
% = 5% and Type II error is =
DC
C
+
% = 2.5%
11. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 3, June 2015
93
In order to test the predictive capability of model, paired samples are 20 for forecasting test.
Predicted results denoted as table 8.
Table 8: Robustness of model (forecasting sample)
No-distressed
Observed value
Non-distressed
company
Distressed company Accuracy of
Classification (%)
Non-distressed company 16 4 80
Distressed company 3 17 85
Overall accuracy of
classification
82.25
Type I error is
BA
B
+
% = 20% and Type II error is =
DC
C
+
% = 15%
4.4. Logistics regression model training and forecasting
Model input values of paired samples are 40. Vector )8,7,6,5,4,3,1,1( zzzzzzzz is input of this
model. The Logistic regression is been realized using Spss21.0 software and the Wald statistic
for test the βˆ coefficient. Since
=2χ Wald = 2]/[ βσβ =24.123
It has 0.1% significant effective. The logistic regression model is:
]}8675.07985.0655.25308.04879.03295.02654.01286.10448.0exp[1{1)( zzzzzzzziyp −+−+−−−−−+=
Table 9 is denoted as the results of logistic regression.
Table 9: The results of Logistic regression model
Variable coefficient Standard
deviation
Wald statistic Degree
freedom
probability
z1 -1.286 0.413 10.356 1 0.001
z2 -0.654 0.346 3.378 1 0.056
z3 --0.295 0.276 1.211 1 0.294
z4 -0.879 1.421 0.389 1 0.531
z5 0.308 0.324 0.926 1 0.334
z6 -2.555 1.692 1.467 1 0.224
z7 0.985 0.268 0.152 1 0.716
z8 -0.675 0.311 4.971 1 0.025
constant -0.448 0.388 1.273 1 0.251
Table 10 and table 11 are robustness of model in training and forecasting samples respectively.
Table 10: Robustness of model (training sample)
No-distressed
Observed value
Non-distressed
company
Distressed company Accuracy of
Classification (%)
Non-distressed company 30 10 75
Distressed company 8 32 80
Overall accuracy of
classification
77.5
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94
Type I error is
BA
B
+
% = 25% and Type II error is =
DC
C
+
% = 20%
Table 11: Robustness of model (forecasting sample)
No-distressed
Observed value
Non-distressed
company
Distressed company Accuracy of
Classification (%)
Non-distressed company 14 6 70
Distressed company 3 17 85
Overall accuracy of
classification
77.5
Type I error is
BA
B
+
% = 30% and Type II error is =
DC
C
+
% = 15%
4.5. Comparison of Wavelet Network and Logistic Regression
In comparison of Wavelet Network and Logistic Regression on training and forecasting sample,
from the table 7, table 8 and table 10, table 11, no matter how the overall prediction accuracy,
Type Ⅰerror and Type Ⅱerror, wavelet networks model is better than logistic regression model.
5. CONCLUSION
This paper select sample listed companies from 2006 to 2009 for training sample. There select 40
paired samples for analysis. It randomly selected 20 paired sample listed companies from 2009 to
2012 for forecasting sample.
Wavelet method and Logistic regression are introduced to suppress the financial distress
prediction accuracy degradation due to potential accounting noise. In this paper, it use
Kolmogorov-Smirnov test (K-S test), Wilcoxon test, Principal component analysis (PCA) to
select potential predictor variables that are informative and closely related to companies’ financial
condition.
In this paper, it has two experiments to compare the prediction accuracy between Wavelet
methods with Logistic regression method. From the results of training and forecasting sample
test, the wavelet networks model in this paper on enterprise’s financial distress early warning is
valid. The result of comparison of Wavelet Network and Logistic Regression denoted as table 12.
Table 12: Comparison of Wavelet Network and Logistic Regression
Model Training Forecasting
Wavelet
Network model
Type errorⅠ 0.05 0.2
Type errorⅡ 0.025 0.15
Overall accuracy of
classification
0.9625 0.8225
Logistic
Regression
Type errorⅠ 0.25 0.30
Type errorⅡ 0.2 0.15
Overall accuracy of
classification
0.775 0.775
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95
In Table 12, the comparison of Wavelet Network and Logistic Regression on training and
forecasting sample, no matter how the overall prediction accuracy, Type Ⅰerror and Type Ⅱ
error, wavelet networks model is better than logistic regression model.
ACKNOWLEDGEMENTS
I would like to thank the anonymous reviewers for their constructive comments on this paper.
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