A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
A HYBRID COA-DEA METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMS ijcsa
The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA and the output-oriented approach of it.The selection criterion is higher efficiency for next iteration of the proposed hybrid method. So the profit function of the COA is replaced by the efficiency value that is obtained from DEA. This algorithm is
compared with other methods using some test problems. The results shows using COA and DEA approach for solving multi-objective problems increases the speed and the accuracy of the generated solutions.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
A HYBRID COA-DEA METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMS ijcsa
The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA and the output-oriented approach of it.The selection criterion is higher efficiency for next iteration of the proposed hybrid method. So the profit function of the COA is replaced by the efficiency value that is obtained from DEA. This algorithm is
compared with other methods using some test problems. The results shows using COA and DEA approach for solving multi-objective problems increases the speed and the accuracy of the generated solutions.
The pertinent single-attribute-based classifier for small datasets classific...IJECEIAES
Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attributebased-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).
Hybrid Multi-Gradient Explorer Algorithm for Global Multi-Objective OptimizationeArtius, Inc.
Hybrid Multi-Gradient Explorer (HMGE) algorithm for global multi-objective
optimization of objective functions considered in a multi-dimensional domain is presented. The proposed hybrid algorithm relies on genetic variation operators for creating new solutions, but in addition to a standard random mutation operator, HMGE
uses a gradient mutation operator, which improves convergence. Thus, random mutation helps find global Pareto frontier, and gradient mutation improves convergence to the
Pareto frontier. In such a way HMGE algorithm combines advantages of both
gradient-based and GA-based optimization techniques: it is as fast as a pure gradient-based MGE algorithm, and is able to find the global Pareto frontier similar to genetic algorithms
(GA). HMGE employs Dynamically Dimensioned Response Surface Method (DDRSM) for calculating gradients. DDRSM dynamically recognizes the most significant design variables, and builds local approximations based only on the variables. This allows one to
estimate gradients by the price of 4-5 model evaluations without significant loss of accuracy. As a result, HMGE efficiently optimizes highly non-linear models with dozens and hundreds of design variables, and with multiple Pareto fronts. HMGE efficiency is 2-10
times higher when compared to the most advanced commercial GAs.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
Threshold benchmarking for feature ranking techniquesjournalBEEI
In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.
Enhancing three variants of harmony search algorithm for continuous optimizat...IJECEIAES
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
Flavours of Physics Challenge: Transfer Learning approachAlexander Rakhlin
Presentation for "Heavy Flavour Data Mining workshop", February 18-19, University of Zurich. I discuss the solution that won Physics Prize of Flavours of Physics challenge organized by CERN, Yandex, Intel at Kaggle.
Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Prob...Waqas Tariq
This paper proposes a metaheuristic to solve the permutation flow shop scheduling problem where several criteria are to be considered, such as: the makespan, total flowtime and total tardiness of jobs. The proposed metaheuristic is based on tabu search algorithm. The Compromise Programming model and the concept of satisfaction functions are utilized to integrate explicitly the Manager’s preferences. The proposed approach has been tested through a computational experiment. This approach can be useful for large scale scheduling problems and the Manager can consider additional scheduling criteria.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Improved feature selection using a hybrid side-blotched lizard algorithm and ...IJECEIAES
Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of sideblotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
The pertinent single-attribute-based classifier for small datasets classific...IJECEIAES
Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attributebased-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).
Hybrid Multi-Gradient Explorer Algorithm for Global Multi-Objective OptimizationeArtius, Inc.
Hybrid Multi-Gradient Explorer (HMGE) algorithm for global multi-objective
optimization of objective functions considered in a multi-dimensional domain is presented. The proposed hybrid algorithm relies on genetic variation operators for creating new solutions, but in addition to a standard random mutation operator, HMGE
uses a gradient mutation operator, which improves convergence. Thus, random mutation helps find global Pareto frontier, and gradient mutation improves convergence to the
Pareto frontier. In such a way HMGE algorithm combines advantages of both
gradient-based and GA-based optimization techniques: it is as fast as a pure gradient-based MGE algorithm, and is able to find the global Pareto frontier similar to genetic algorithms
(GA). HMGE employs Dynamically Dimensioned Response Surface Method (DDRSM) for calculating gradients. DDRSM dynamically recognizes the most significant design variables, and builds local approximations based only on the variables. This allows one to
estimate gradients by the price of 4-5 model evaluations without significant loss of accuracy. As a result, HMGE efficiently optimizes highly non-linear models with dozens and hundreds of design variables, and with multiple Pareto fronts. HMGE efficiency is 2-10
times higher when compared to the most advanced commercial GAs.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
Threshold benchmarking for feature ranking techniquesjournalBEEI
In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.
Enhancing three variants of harmony search algorithm for continuous optimizat...IJECEIAES
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
Flavours of Physics Challenge: Transfer Learning approachAlexander Rakhlin
Presentation for "Heavy Flavour Data Mining workshop", February 18-19, University of Zurich. I discuss the solution that won Physics Prize of Flavours of Physics challenge organized by CERN, Yandex, Intel at Kaggle.
Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Prob...Waqas Tariq
This paper proposes a metaheuristic to solve the permutation flow shop scheduling problem where several criteria are to be considered, such as: the makespan, total flowtime and total tardiness of jobs. The proposed metaheuristic is based on tabu search algorithm. The Compromise Programming model and the concept of satisfaction functions are utilized to integrate explicitly the Manager’s preferences. The proposed approach has been tested through a computational experiment. This approach can be useful for large scale scheduling problems and the Manager can consider additional scheduling criteria.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Improved feature selection using a hybrid side-blotched lizard algorithm and ...IJECEIAES
Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of sideblotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Integrated bio-search approaches with multi-objective algorithms for optimiza...TELKOMNIKA JOURNAL
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the
manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
known makespan in 17 out of 20 problems.
Best-worst northern goshawk optimizer: a new stochastic optimization methodIJECEIAES
This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
ATTRIBUTE REDUCTION-BASED ENSEMBLE RULE CLASSIFIERS METHOD FOR DATASET CLASSI...csandit
Attribute reduction and classification task are an essential process in dealing with large data
sets that comprise numerous number of input attributes. There are many search methods and
classifiers that have been used to find the optimal number of attributes. The aim of this paper is
to find the optimal set of attributes and improve the classification accuracy by adopting
ensemble rule classifiers method. Research process involves 2 phases; finding the optimal set of
attributes and ensemble classifiers method for classification task. Results are in terms of
percentage of accuracy and number of selected attributes and rules generated. 6 datasets were
used for the experiment. The final output is an optimal set of attributes with ensemble rule
classifiers method. The experimental results conducted on public real dataset demonstrate that
the ensemble rule classifiers methods consistently show improve classification accuracy on the
selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
The approaches used in literature for solving combinatorial optimization problems have applied specific methodology or a
specific combination of methodologies to solve it. However, less importance is attached to modeling the solution for the given problem systematically. Modeling helps in analyzing the various parts of the solution clearly, thereby identifying which part of the methodology or combination of methodologies applied is efficient or inefficient. In order to find how efficient the different parts of the applied methodology is or methodologies are, it may be better to solve the given problem using the notion of hyper-heuristics. This can be done by solving the different parts of the given problem with many different methodologies realized, implemented and benchmarked, enabling to choose the best hybrid methodology. A theoretical model or representation of the problem’s solution may facilitate clear proposal and realization of the different methodologies for the various parts of the solution. The literature reveals that there is a need
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MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Similar to BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA (20)
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Call for Papers - Advanced Computing An International Journal (ACIJ) (2).pdfacijjournal
Submit your Research Papers!!!
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Advanced Computing: An International Journal (ACIJ
)
is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advancedcomputing. The journal focuses on all technical and practical aspects of high performancecomputing, green computing, pervasive computing, cloud computing etc. The goal of this journalis to bring together researchers anda practitioners from academia and industry to focus onunderstanding advances in computing and establishing new collaborations in these areas
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
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BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA
1. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
DOI:10.5121/acij.2019.10501 1
BINARY SINE COSINE ALGORITHMS FOR FEATURE
SELECTION FROM MEDICAL DATA
Shokooh Taghian1,2
and Mohammad H. Nadimi-Shahraki1,2,*
1
Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University,
Najafabad, Iran
2
Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
ABSTRACT
A well-constructed classification model highly depends on input feature subsets from a dataset, which may
contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical
datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select
the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find
optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine
Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature
selection from the medical data. In these algorithms, the search space remains continuous, while a binary
position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The
proposed algorithms are compared with four latest binary optimization algorithms over five medical
datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance
the accuracy of classification on these medical datasets compared to four other algorithms.
KEYWORDS
Medical data, Feature selection, metaheuristic algorithm, Sine Cosine Algorithm, Transfer function.
1. INTRODUCTION
By advancing in the technology, a massive amount of data is regularly generated and stored from
real-world applications such as medical, transportation, tourism and engineering. This massive
data contains a large number of different features. However, not all the features are needed for
analyzing and discovering knowledge, since many of them are redundant or irrelevant to the
problem. Many redundant or irrelevant features are not effective for solving classification
problems; moreover, they may increase the computational complexity and decrease the
classification accuracy [1, 2].
Dimensionality reduction is one of the most important preprocessing techniques, which aims to
reduce the number of features under some criterion and obtain a better performance. One of the
most important tools in dimension reduction is the feature selection [3]. Feature selection is the
process of selecting more effective and relevant features in order to reduce the dimensionality of
data and improve the classification performance [4]. As shown in Fig. 1, feature selection has
four main phases including, subset generation, subset evaluation, stopping criteria, and validation
[5]. In the first step, the search strategy employs different methods in order to generate a new
subset as a solution. The second step includes a classifier and a predefined fitness function to
evaluate the quality of the generated solutions. This process continues until the termination
criteria are met. In the literature, there are two different approaches filter and wrapper to select the
effective features [6]. The former approach uses measures such as distance, dependency, or
2. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
2
consistency of the features to find the optimal subset. The latter uses a specific classifier to
evaluate the quality of selected features and find the near-optimal solutions from an exponential
set of features. The major drawback of filter method is that it lacks the influence of features on
the performance of the classifier [7]; however, since it does not use the learning algorithms, it is
usually fast and suitable for use with large data sets. On the other hand, the wrapper method is
known to be more accurate but it is computationally more expensive [8].
The feature selection is to find the optimum combination of features; therefore, it can be
considered as a search process. Since evaluating 2N
-1 subsets of a dataset with N features is an
NP-hard problem, finding the best subset cannot be achieved using an exhaustive search
algorithm. Metaheuristic algorithms are known for their ability in finding near-optimum solutions
for global optimization problems within a reasonable time. These algorithms can exploit the
solution with good fitness and have the potential of finding promising areas.
Figure 1. Feature selection process
However, due to the random nature of the metaheuristic algorithms, there is no guarantee that
they can find the optimal feature subset for different problems [9]. Additionally, according to the
No-Free-Lunch theorem [10], there is no single, all-purpose, and general optimization algorithm,
which can find optimum solutions for all problems. Therefore, many metaheuristic algorithms
have been proposed for solving continuous problems such as particle swarm optimization (PSO)
[11], differential evolution (DE) [12], artificial bee colony (ABC) [13], bat algorithm (BA) [14],
gravitational search algorithm (GSA) [15], grey wolf optimizer (GWO) [16], and sine cosine
algorithm (SCA) [17]. Also, with increasing the number of variables and complexity of the
problems, the high dimensional problems are an emerging issue, and recently some metaheuristic
algorithms such as conscious neighborhood-based crow search algorithm (CCSA) [18] have been
proposed for solving large-scale optimization problems. Some algorithms such as genetic
algorithm [19] and ant colony algorithm (ACO) [20] were proposed for solving the discrete
optimization problems. Meanwhile, different methods were introduced to adapt a continuous
metaheuristic algorithm for a discrete search space [21]. Because of having the successful results
of metaheuristic algorithms, they are widely applied to solve a variety of discrete and continuous
optimization [22-26].
Sine Cosine Algorithm was recently proposed for continuous optimization problems which
attracts the attention of many researchers to use its potentials and apply to different applications.
Although some binary variants of the SCA were proposed for discrete optimization problems,
there is no variant of this algorithm for feature selection from medical datasets. This is our
motivation to develop another binary version of the SCA.
The rest of this paper is organized as follow: a review of the literature on binary metaheuristic
algorithms used in feature selection problem is explained in Section 2. In Section 3 the
3. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
3
continuous Sine Cosine Algorithm is described. The proposed binary versions of sine cosine
algorithm describe in Section4. The proposed algorithms for feature selection problem are
presented in Section 5 and the experimental results are reported in Section 6. Finally, the
conclusion and future works are stated in Section 7 contains.
2. RELATED WORKS
In the past decade, metaheuristic algorithms attract attention of many researchers due to their
powerful and efficient performance in dealing with complex real-world problems [27]. A great
deal of efforts has been made to solve various problems in different fields. However, many well-
known metaheuristic algorithms are designed for solving continuous problems, while some
problems have a binary nature. Therefore, binary versions of these algorithms were developed to
solve these problems. Most of the well-known metaheuristic algorithms have the binary version
which makes them capable of solving binary problems.
In the literature, different methods exist to develop a binary algorithm such as normalization,
rounding, considering a binary search space, and using binary operators. In addition, the transfer
function is another method that is used to modify the value of continuous components into binary
values [21]. BGSA [28] is a binary version of the GSA, which used a V-shaped transfer function
applied on the velocity parameter in order to calculate the mass movement probability. In [29], a
binary version of GWO was combined with KNN classifier to calculate the fitness function of
each features subset. An S-shaped transfer function is applied on the position of each wolf to
estimate the position changing. In [30], the continuous Dragonfly algorithm (DA) [31] was
modified to tackle the feature selection problem. This is performed by using the V-shaped transfer
function that is applied on the step vector value of each search agent. Binary Salp Swarm
Algorithm (SSA) [32] is a recent binary metaheuristic algorithm, that uses S-shaped and V-
shaped transfer functions to modify the algorithm in order to solve feature selection problems.
Lately, Whale Optimization Algorithm [33] has been employed as a feature selection algorithm
for disease detection [34]. The binary butterfly [35], hybrid GWO with CSA [36], and
evolutionary GSA [37] are some example of newly proposed binary algorithms that used for
feature selection problem. For the SCA, two other variants are proposed with binary variables. In
[38], a binary version of SCA is proposed by using the rounding method. Variables of this
algorithm are bounded to 0 and 1, therefore each value of the solution is rounded to the nearest
value to show the feature is selected or not. In the other work [39], a modified sigmoid function
used as a mapped function to solve binary problems.
In this work, the focus is on using transfer functions to produce a binary version of the SCA for
wrapper feature selection. The proposed algorithms select the optimal subset of features which
increase the accuracy of the classifier and at the same time decrease the length of feature subset.
The application of proposed algorithms then applied for disease detection by using UCI medical
datasets [40].
3. THE SINE COSINE ALGORITHM (SCA)
The SCA is a population-based metaheuristic algorithm introduced for solving continuous
optimization problems. The SCA starts with randomly distributing the solutions in the search
space. After calculating the fitness value of each solution, the solution with the best fitness is
considered as a destination solution. The destination solution is used in a position update equation
shown by Eq. 1 by which the position of other solutions is changed.
4. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
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5.0,)cos(
5.0,)sin(
4321
43211
rXPrrrX
rXPrrrX
X t
i
t
i
t
i
t
i
t
i
t
it
i (1)
where t
iX is the value of i-th dimension of the current solution at iteration t, t
iP is the position of
the destination solution in i-th dimension and t-th iteration, and r1, r2, and r3 are random numbers.
In this equations, one formula is selected by a random number r4, which is uniformly distributed
between 0 and 1. The SCA runs until the termination criteria is met.
The SCA controls the exploration and exploitation of the algorithm and direct the solution to the
next position using three parameters r1, r2, and r3. The parameter r1 has the ability of balancing the
exploration and exploitation in the early and last stages of the SCA. This parameter determines
the direction of the new solution either toward or outward the destination solution. It directs the
search process, whether to explore the entire search space even far from the destination solution
in the early stages of the algorithm or to exploit near the destination solution in order to find
better solutions in the last stages of the algorithm. If r1 < 0 the distance between the solution and
the destination solution will be decreased, while it will be increased if r1 > 0. The r1 parameter is
calculated by considering the maximum iterations T, the current iteration t, and a constant value a
as shown in Eq. 4.
t
a
tar 1 (2)
The random parameter r2 indicates the distance value of the solution from the destination solution
position. The higher value of this parameter leads to exploration because the distance between the
solution and the destination solution is more, while the lower value indicates the less distance and
leads to exploitation. The third parameter r3 is a weight to show the impact of the destination
solution in defining the distance.
4. BINARY SINE COSINE ALGORITHM (BSCA)
The original SCA is to solve the continuous optimization problems where each individual can
move freely in the entire search space; while a binary search space can be assumed as a
hypercube that the individuals can only move to neither nearer or farther corners of the hypercube
by flipping the bit-string position value [41]. Therefore, to use the SCA for solving binary
problems, it must map the continuous values into the probability values using a transfer function
in order to determine the binary position values. As discussed in our previous work [our work],
two introduced families of the transfer functions are S-shaped and V-shaped. In this work, the
transfer functions are utilized to convert the continuous SCA to binary versions which named
SBSCA and VBSCA.
4.1. S-shaped Binary Sine Cosine Algorithm (SBSCA)
The search space in the proposed algorithms is considered as a continuous space in which each
individual has a floating-point position vector. Therefore, to generate the individual’s binary bit-
string, the continuous values must be converted. The conversion is applied by using an S-shaped
transfer function on each dimension of the position to force the individual to move in a binary
space. The transfer function uses the floating-point position values to determine a bounded
probability in the interval of [0, 1] for each individual. The probabilities then are used to generate
bit-string position vector from a floating-point vector. The equation and the shape of the S-shaped
function are given in Eq. 3 and Fig 2a.
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)(
1
1
))1((
tx
d
i d
i
e
txS
(3)
The value of ))1(( txS d
i indicates the probability of changing the binary position value of i-th
individual in d-th dimension. Then, the probability, as mentioned in Eq. 4 compared with a
threshold value to determine the binary value.
otherwise
txSrandif
tx
d
id
i
,0
))1((,1
)1( (4)
4.2. V-shaped Binary Sine Cosine Algorithm (VBSCA)
The V-shaped transfer function is the other function which is used for calculating the position
changing probabilities. The V-shaped transfer function shown in Fig. 2b, like the S-shaped
transfer function, is first utilized for calculating the probability of changing the individual’s
positions by Eq.5.
))()(
2
arctan(
2
))1(( txtxV d
i
d
i
(5)
After estimating the probability values, a new updating position equation is employed to update
the binary position vector of each individual, as shown in Eq. 6.
otherwisetx
txVrandiftxcomplement
tx d
i
d
i
d
id
i
,)(
))1((,))((
)1( (6)
5. BINARY SINE COSINE ALGORITHM FOR THE FEATURE SELECTION
PROBLEM
Feature selection is a process of selecting relevant features of a dataset in order to improve the
learning performance, decreasing the computational complexity, and building a better
classification model. Based on the nature of the feature selection problem, a binary algorithm is
Figure 1. Figure 2. (a) S-shaped transfer function (b) V-shaped transfer function
6. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
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usually applied to find an optimum feature subset. Every individual in the binary algorithms is
represented as a binary vector with N entries, where N is the total number of features in a dataset.
Each vector has the value 0 or 1, where zero indicates that the feature is not selected whereas one
represents that the feature is selected. For this reason, in this work, two proposed binary versions
of the SCA are applied in the feature selection problem.
Feature selection can be considered as a multi-objective problem in which two contrary objectives
must be satisfied. These two objectives are the maximum accuracy, and the other is the minimum
number of selected features. The fitness function that is used to evaluate each individual is shown
in Eq.7.
C
R
DEFitness R )( (7)
where )(DER is the classification error, R is the number of selected features, C is the total
number of features in the dataset, and are two parameters related to the importance of
accuracy and number of selected features, [0, 1] and 𝛽=1-α [29].
6. EXPERIMENTAL EVALUATION
6.1. Experimental settings
In this section, the performance of the SBSCA and VBSCA algorithms are evaluated and
compared to other binary algorithm exists in the literature. In order to validate the experiment,
five UCI datasets are selected with various number of features and instances. Table 1 depicts the
details of each dataset. For the evaluation process, each dataset is split into %80 for training and
%20 for testing. All the experiments were repeated for 30 runs to obtain meaningful results. In
this work, k-nearest neighbor classifier (KNN) is used to indicate the classification error rate of
the selected feature subset with k=5. All the experiments are performed on PC with Intel
Core(TM) i7-3770 3.4GHz CPU and 8.00 GB RAM using MATLAB 2014 software.
The proposed SBSCA and VBSCA are compared with BBA [42], BGSA, BGWO, and BDA. The
initial and specific parameters of each algorithm are reported in Table 2.
Table 1. List of datasets used in the experiment
Dataset No. of features No. of instances No. of classes
Pima 9 768 2
Breast Cancer 10 683 2
Heart 14 270 2
Lymphography 19 148 4
Breast-WDBC 31 569 2
In order to have a fair comparison, all the algorithms use the same initial settings. Each algorithm
is randomly initialized with the population size, and the number of iterations are set to 20 and
300. The parameter in the fitness function has a value of 0.99. Evaluation criteria for all the
algorithms are considered as average classification accuracy and number of selected features. To
7. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
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prove the significance of the proposed algorithms over other algorithms a non-parametric
statistical test called Friedman test [43] is conducted as well.
Table 2. Parameter settings for algorithms
6.2. Numerical Results
In this section, the results of the proposed binary versions of the SCA, SBSCA, and VBSCA are
compared with other binary metaheuristic algorithms which are widely used to solve the feature
selection problem. Table 3 outlines the result of BBA, BGSA, BGWO, BDA, SBSCA, and
VBSCA based on the average and standard deviation of the accuracy. Note that the best results
are highlighted in bold. As per results reported in Table 3, the SBSCA algorithm provides the
competitive or even better results on all the datasets. It achieves the same results like BGSA on
Pima and Breast Cancer datasets, while outperforms all other algorithms on Heart,
Lymphography, and Breast-WDBC datasets.
Table 3. Comparison between the SBSCA, VBSCA and other binary metaheuristic algorithms based on
average accuracy
Dataset BBA BGSA BGWO BDA SBSCA VBSCA
Pima
AVE 0.7541 0.7727 0.7667 0.6697 0.7727 0.7727
STD 0.0119 0.0000 0.0098 0.1120 0.0000 0.0000
Breast Cancer
AVE 0.9983 1.0000 0.9998 0.8659 1.0000 1.0000
STD 0.0031 0.0000 0.0013 0.1038 0.0000 0.0000
Heart
AVE 0.8525 0.8772 0.8716 0.6975 0.8963 0.8926
STD 0.0179 0.0091 0.0257 0.2372 0.0092 0.0075
Lymphography
AVE 0.7978 0.8344 0.8300 0.7978 0.8767 0.8633
STD 0.0230 0.0205 0.0268 0.2174 0.0250 0.0202
Breast-WDBC
AVE 0.9518 0.9591 0.9532 0.9556 0.9673 0.9655
STD 0.0060 0.0048 0.0066 0.0679 0.0046 0.0022
Friedman Test 5.30 2.60 4.20 5.50 1.40 2.00
Table 4 shows the average and standard deviation of the number of selected features. It can be
observed that the two proposed binary algorithms nearly have the same performance in term of
the number of selected features. Moreover, the SBSCA, VBSCA, and the BDA obtained the best
result on the Breast cancer dataset, while BDA had a competitive result on the Breast-WDBC and
Lymphography datasets.
Algorithm Parameter Value
BBA
minQ 0
maxQ 2
A 0.5
r 0.5
BGSA 0G 100
bGWO a [2 0]
bSCA a 2
8. Advanced Computing: An International Journal (ACIJ), Vol.10, No.1/2/3/4/5, September 2019
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Table 4. Comparison between the SBSCA, VBSCA and other binary metaheuristic algorithms based on
average number of selected features
Dataset BBA BGSA BGWO BDA SBSCA VBSCA
Pima
AVE 3.00 5.00 5.10 5.00 5.00 5.00
STD 1.53 0.00 0.31 0.00 0.00 0.00
Breast Cancer
AVE 3.27 3.20 4.27 3.00 3.00 3.00
STD 1.41 0.41 1.08 0.00 0.00 0.00
Heart
AVE 5.07 4.97 7.03 5.33 5.27 5.27
STD 2.07 1.16 0.76 1.42 1.46 1.41
Lymphography
AVE 6.33 7.63 9.73 6.03 6.13 7.23
STD 3.07 1.73 2.21 1.33 1.55 2.06
Breast-WDBC
AVE 11.10 12.77 11.93 4.27 4.20 9.33
STD 3.39 2.51 2.46 1.14 1.06 2.25
Friedman Test 3.00 3.90 5.80 2.70 2.40 3.20
7. CONCLUSIONS
In this paper, two binary variants of the Sine Cosine Algorithm (SCA) were proposed and used to
find the effective features in the wrapper approach. The continuous version of the SCA is
converted either by S-shaped and V-shaped transfer functions to develop two binary algorithms
SBSCA and VBSCA. The proposed algorithms are employed in feature selection problem for
disease detection using KNN classifier. The SBSCA, VBSCA and the four state-of-the art binary
algorithms are applied on five medical datasets from UCI repository and their results are
compared. The experimental results show that the SBSCA algorithm is able to compete and/or
achieves better results compared to other algorithms on most of the datasets. For future studies,
the bSCA versions can be applied to various public datasets with different classifiers, and real-
world problems. Furthermore, it would be interesting to use the bSCA in solving problems with
multiple objectives.
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AUTHORS
Dr. Mohammad-Hossein Nadimi-Shahraki received his Ph.D. in computer
science-artificial intelligence from University Putra of Malaysia (UPM) in
2010. Currently, he is an Ass professor in faculty of computer engineering
and a senior data scientist in Big Data Research Center in Islamic Azad
University of Najafabad (IAUN). His research interests include big data
analytics, data mining algorithms, medical data mining, machine learning
and metaheuristic algorithms.
Ms. Shokooh Taghian was born in Iran. She received M.S. degrees in
computer software engineering from the faculty of computer engineering
in IAUN. She is currently researching as a research assistant and
developer in Big Data Research Center in (IAUN). Her research interests
focus on metaheuristic algorithms, machine learning and medical data
analysis.