This document discusses using the ID3 decision tree algorithm to evaluate research scholars based on feedback from their guides/advisors. It begins by describing the problem and how a dataset is formed using attributes about scholars and feedback from guides. It then provides an overview of the ID3 algorithm and how it works. The document applies the ID3 algorithm to the scholar evaluation dataset to construct a decision tree, which can then be used to determine a guide's overall view of a scholar based on their attribute values. The tree can also provide scholars with guidelines on areas to improve to achieve a better evaluation.
Research scholars evaluation based on guides view using id3eSAT Journals
Abstract Research Scholars finds many problems in their Research and Development activities for the completion of their research work in universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by considering this constructed tree to improve their outcomes.
Implementation of Improved ID3 Algorithm to Obtain more Optimal Decision Tree.IJERD Editor
Decision tree learning is the discipline to create a predictive model to map the different items in the
set and respective target values and associate them in a way that is true to every element. This concept is used in
statistics, data mining and machine learning due to its simple and effectiveness.
Among the various strategies available to construct the decision trees ID3 is one of the simplest and
most widely used decision tree algorithm, but ID3 algorithm gives more importance to attributes having
multiple values while selecting node. This major shortcoming affects the accuracy of decision tree. In this paper
we are proposing improvement in ID3 algorithm using association function (AF). The Experimental result
shows improved ID3 algorithm can overcome shortcomings of ID3 which will also improve the accuracy of ID3
algorithm.
Deployment of ID3 decision tree algorithm for placement predictionijtsrd
This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a students profile such as CGPA, academic performance, technical and communication skills and design a model which can predict the placement of a student. For this purpose ID3 classification technique based on decision tree has been used. Kirandeep | Prof. Neena Madan"Deployment of ID3 decision tree algorithm for placement prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11073.pdf http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
Hypothesis on Different Data Mining AlgorithmsIJERA Editor
In this paper, different classification algorithms for data mining are discussed. Data Mining is about
explaining the past & predicting the future by means of data analysis. Classification is a task of data mining,
which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
four different classification approaches namely Frequency Table, Covariance Matrix, Similarity Functions &
Others. As work for research on classification methods, algorithms like Naive Bayesian, K Nearest Neighbors,
Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
datasets like Iris & Lung Cancer.
Privacy preserving data mining in four group randomized response technique us...eSAT Journals
Abstract Data mining is a process in which data collected from different sources is analyzed for useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%.
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
Research scholars evaluation based on guides view using id3eSAT Journals
Abstract Research Scholars finds many problems in their Research and Development activities for the completion of their research work in universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by considering this constructed tree to improve their outcomes.
Implementation of Improved ID3 Algorithm to Obtain more Optimal Decision Tree.IJERD Editor
Decision tree learning is the discipline to create a predictive model to map the different items in the
set and respective target values and associate them in a way that is true to every element. This concept is used in
statistics, data mining and machine learning due to its simple and effectiveness.
Among the various strategies available to construct the decision trees ID3 is one of the simplest and
most widely used decision tree algorithm, but ID3 algorithm gives more importance to attributes having
multiple values while selecting node. This major shortcoming affects the accuracy of decision tree. In this paper
we are proposing improvement in ID3 algorithm using association function (AF). The Experimental result
shows improved ID3 algorithm can overcome shortcomings of ID3 which will also improve the accuracy of ID3
algorithm.
Deployment of ID3 decision tree algorithm for placement predictionijtsrd
This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a students profile such as CGPA, academic performance, technical and communication skills and design a model which can predict the placement of a student. For this purpose ID3 classification technique based on decision tree has been used. Kirandeep | Prof. Neena Madan"Deployment of ID3 decision tree algorithm for placement prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11073.pdf http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
Hypothesis on Different Data Mining AlgorithmsIJERA Editor
In this paper, different classification algorithms for data mining are discussed. Data Mining is about
explaining the past & predicting the future by means of data analysis. Classification is a task of data mining,
which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
four different classification approaches namely Frequency Table, Covariance Matrix, Similarity Functions &
Others. As work for research on classification methods, algorithms like Naive Bayesian, K Nearest Neighbors,
Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
datasets like Iris & Lung Cancer.
Privacy preserving data mining in four group randomized response technique us...eSAT Journals
Abstract Data mining is a process in which data collected from different sources is analyzed for useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%.
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
Efficient classification of big data using vfdt (very fast decision tree)eSAT Journals
Abstract
Decision Tree learning algorithms have been able to capture knowledge successfully. Decision Trees are best considered when
instances are described by attribute-value pairs and when the target function has a discrete value. The main task of these
decision trees is to use inductive methods to the given values of attributes of an unknown object and determine an
appropriate classification by applying decision tree rules. Decision Trees are very effective forms to evaluate the performance
and represent the algorithms because of their robustness, simplicity, capability of handling numerical and categorical data,
ability to work with large datasets and comprehensibility to a name a few. There are various decision tree algorithms available
like ID3, CART, C4.5, VFDT, QUEST, CTREE, GUIDE, CHAID, CRUISE, etc. In this paper a comparative study on three of
these popular decision tree algorithms - (Iterative Dichotomizer 3), C4.5 which is an evolution of ID3 and VFDT (Very
Fast Decision Tree has been made. An empirical study has been conducted to compare C4.5 and VFDT in terms of accuracy
and execution time and various conclusions have been drawn.
Key Words: Decision tree, ID3, C4.5, VFDT, Information Gain, Gain Ratio, Gini Index, Over−fitting.
Analysis of Classification Algorithm in Data Miningijdmtaiir
Data Mining is the extraction of hidden predictive
information from large database. Classification is the process
of finding a model that describes and distinguishes data classes
or concept. This paper performs the study of prediction of class
label using C4.5 and Naïve Bayesian algorithm.C4.5 generates
classifiers expressed as decision trees from a fixed set of
examples. The resulting tree is used to classify future samples
.The leaf nodes of the decision tree contain the class name
whereas a non-leaf node is a decision node. The decision node
is an attribute test with each branch (to another decision tree)
being a possible value of the attribute. C4.5 uses information
gain to help it decide which attribute goes into a decision node.
A Naïve Bayesian classifier is a simple probabilistic classifier
based on applying Baye’s theorem with strong (naive)
independence assumptions. Naive Bayesian classifier assumes
that the effect of an attribute value on a given class is
independent of the values of the other attribute. This
assumption is called class conditional independence. The
results indicate that Predicting of class label using Naïve
Bayesian classifier is very effective and simple compared to
C4.5 classifier
Enhanced ID3 algorithm based on the weightage of the AttributeAM Publications
ID3 algorithm a decision tree classification algorithm is very popular due to its speed and simplicity in construction but it has its own snags while classifying the ID3 algorithm and tends to choose the attributes with large values and practical complexities arises due to this. To solve this problem the proposed algorithm empowers and uses the importance of the attributes and classifies accordingly to produce effective rules. The proposed algorithm uses the attribute weightage and calculates the information gain for the few values attributes and performs quite better when compared to classical ID3 algorithm. The proposed algorithm is applied on a real time data (i.e.) selection process of employees in a firm for appraisal based on few important attributes and executed.
Comparative study of various supervisedclassification methodsforanalysing def...eSAT Publishing House
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.
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINEaciijournal
Today, enormous amount of data is collected in medical databases. These databases may contain valuable
information encapsulated in nontrivial relationships among symptoms and diagnoses. Extracting such
dependencies from historical data is much easier to done by using medical systems. Such knowledge can be
used in future medical decision making. In this paper, a new algorithm based on C4.5 to mind data for
medince applications proposed and then it is evaluated against two datasets and C4.5 algorithm in terms of
accuracy.
Comparative study of ksvdd and fsvm for classification of mislabeled dataeSAT Journals
Abstract Outlier detection is the important concept in data mining. These outliers are the data that differ from the normal data. Noise in the
application may cause the misclassification of data. Data are more likely to be mislabeled in presence of noise leading to
performance degradation. The proposed work focuses on these issues. Data before classifying is given a value that represents its
willingness towards the class. This data with likelihood value is then given to classifier to predict the data. SVDD algorithm is
used for classification of data with likelihood values.
Keywords: Confusion Matrix, FSVM, Outlier, Outlier Detection, SVDD
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
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.
Semantic approach utilizing data mining and case based reasoning for it suppo...eSAT Publishing House
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
Efficient classification of big data using vfdt (very fast decision tree)eSAT Journals
Abstract
Decision Tree learning algorithms have been able to capture knowledge successfully. Decision Trees are best considered when
instances are described by attribute-value pairs and when the target function has a discrete value. The main task of these
decision trees is to use inductive methods to the given values of attributes of an unknown object and determine an
appropriate classification by applying decision tree rules. Decision Trees are very effective forms to evaluate the performance
and represent the algorithms because of their robustness, simplicity, capability of handling numerical and categorical data,
ability to work with large datasets and comprehensibility to a name a few. There are various decision tree algorithms available
like ID3, CART, C4.5, VFDT, QUEST, CTREE, GUIDE, CHAID, CRUISE, etc. In this paper a comparative study on three of
these popular decision tree algorithms - (Iterative Dichotomizer 3), C4.5 which is an evolution of ID3 and VFDT (Very
Fast Decision Tree has been made. An empirical study has been conducted to compare C4.5 and VFDT in terms of accuracy
and execution time and various conclusions have been drawn.
Key Words: Decision tree, ID3, C4.5, VFDT, Information Gain, Gain Ratio, Gini Index, Over−fitting.
Analysis of Classification Algorithm in Data Miningijdmtaiir
Data Mining is the extraction of hidden predictive
information from large database. Classification is the process
of finding a model that describes and distinguishes data classes
or concept. This paper performs the study of prediction of class
label using C4.5 and Naïve Bayesian algorithm.C4.5 generates
classifiers expressed as decision trees from a fixed set of
examples. The resulting tree is used to classify future samples
.The leaf nodes of the decision tree contain the class name
whereas a non-leaf node is a decision node. The decision node
is an attribute test with each branch (to another decision tree)
being a possible value of the attribute. C4.5 uses information
gain to help it decide which attribute goes into a decision node.
A Naïve Bayesian classifier is a simple probabilistic classifier
based on applying Baye’s theorem with strong (naive)
independence assumptions. Naive Bayesian classifier assumes
that the effect of an attribute value on a given class is
independent of the values of the other attribute. This
assumption is called class conditional independence. The
results indicate that Predicting of class label using Naïve
Bayesian classifier is very effective and simple compared to
C4.5 classifier
Enhanced ID3 algorithm based on the weightage of the AttributeAM Publications
ID3 algorithm a decision tree classification algorithm is very popular due to its speed and simplicity in construction but it has its own snags while classifying the ID3 algorithm and tends to choose the attributes with large values and practical complexities arises due to this. To solve this problem the proposed algorithm empowers and uses the importance of the attributes and classifies accordingly to produce effective rules. The proposed algorithm uses the attribute weightage and calculates the information gain for the few values attributes and performs quite better when compared to classical ID3 algorithm. The proposed algorithm is applied on a real time data (i.e.) selection process of employees in a firm for appraisal based on few important attributes and executed.
Comparative study of various supervisedclassification methodsforanalysing def...eSAT Publishing House
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.
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINEaciijournal
Today, enormous amount of data is collected in medical databases. These databases may contain valuable
information encapsulated in nontrivial relationships among symptoms and diagnoses. Extracting such
dependencies from historical data is much easier to done by using medical systems. Such knowledge can be
used in future medical decision making. In this paper, a new algorithm based on C4.5 to mind data for
medince applications proposed and then it is evaluated against two datasets and C4.5 algorithm in terms of
accuracy.
Comparative study of ksvdd and fsvm for classification of mislabeled dataeSAT Journals
Abstract Outlier detection is the important concept in data mining. These outliers are the data that differ from the normal data. Noise in the
application may cause the misclassification of data. Data are more likely to be mislabeled in presence of noise leading to
performance degradation. The proposed work focuses on these issues. Data before classifying is given a value that represents its
willingness towards the class. This data with likelihood value is then given to classifier to predict the data. SVDD algorithm is
used for classification of data with likelihood values.
Keywords: Confusion Matrix, FSVM, Outlier, Outlier Detection, SVDD
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
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.
Semantic approach utilizing data mining and case based reasoning for it suppo...eSAT Publishing House
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
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.
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.
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
A software framework for dynamic modeling of dc motors at robot jointseSAT Publishing House
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
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
New optimization scheme for cooperative spectrum sensing taking different snr...eSAT Publishing House
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
Conceptual design of laser assisted fixture for bending operationeSAT Publishing House
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
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
Transient voltage distribution in transformer winding (experimental investiga...eSAT Publishing House
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.
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
A simple and effective scheme to find malicious node in wireless sensor networkeSAT Publishing House
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
An iterative unsymmetrical trimmed midpoint median filter for removal of high...eSAT Publishing House
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
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
Study of shape of intermediate sill on the design of stilling basin modeleSAT Publishing House
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
Perfomance Comparison of Decsion Tree Algorithms to Findout the Reason for St...ijcnes
Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, rule mining can be applied on the educational data for predicting the students behavior. This paper focuses on finding thesuitablealgorithm which yields the best result to find out the reason behind students absenteeism in an academic year. The first step in this processis to gather students data by using questionnaire.The datais collected from 123 under graduate students from a private college which is situated in a semirural area. The second step is to clean the data which is appropriate for mining purpose and choose the relevant attributes. In the final step, three different Decision tree induction algorithms namely, ID3(Iterative Dichotomiser), C4.5 and CART(Classification and Regression Tree)were applied for comparison of results for the same data sample collected using questionnaire. The results were compared to find the algorithm which yields the best result in predicting the reason for student s absenteeism.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
Advanced Computational Intelligence: An International Journal (ACII)aciijournal
Today, enormous amount of data is collected in medical databases. These databases may contain valuable
information encapsulated in nontrivial relationships among symptoms and diagnoses. Extracting such
dependencies from historical data is much easier to done by using medical systems. Such knowledge can be
used in future medical decision making. In this paper, a new algorithm based on C4.5 to mind data for
medince applications proposed and then it is evaluated against two datasets and C4.5 algorithm in terms of
accuracy.
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation ...IJECEIAES
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
Abstract In this paper, the concept of data mining was summarized and its significance towards its methodologies was illustrated. The data mining based on Neural Network and Genetic Algorithm is researched in detail and the key technology and ways to achieve the data mining on Neural Network and Genetic Algorithm are also surveyed. This paper also conducts a formal review of the area of rule extraction from ANN and GA. Keywords: Data Mining, Neural Network, Genetic Algorithm, Rule Extraction.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
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
SURVEY ON CLASSIFICATION ALGORITHMS USING BIG DATASETEditor IJMTER
Data mining environment produces a large amount of data that need to be analyzed.
Using traditional databases and architectures, it has become difficult to process, manage and analyze
patterns. To gain knowledge about the Big Data a proper architecture should be understood.
Classification is an important data mining technique with broad applications to classify the various
kinds of data used in nearly every field of our life. Classification is used to classify the item
according to the features of the item with respect to the predefined set of classes. This paper put a
light on various classification algorithms including j48, C4.5, Naive Bayes using large dataset.
GI-ANFIS APPROACH FOR ENVISAGE HEART ATTACK DISEASE USING DATA MINING TECHNIQUESAM Publications
The process of selecting a subset of relevant features from the feature space for use in model construction and used to carry out the feature selection process is called as pre processing step. The filter approach computationally fast and given accuracy results. The Professional Medical Conduct Board Actions data consist of all public actions taken against physicians, physician assistants, specialist assistants, and medical professional. The Classification and Regression Trees (CART), which described the generation of binary decision trees CART were invented independently of one another at around the same time, yet follow a similar approach for learning decision trees from training tuples. The research used GI-ANFIS is used to data mining technique on heart data sets to provide the diagnosis results.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
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among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
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Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
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1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 10 | Oct-2013, Available @ http://www.ijret.org 380
RESEARCH SCHOLARS EVALUATION BASED ON GUIDES VIEW
USING ID3
Sathiyaraj.R1
, Sujatha.V2
1, 2
Assistant Professor, Department of CSE, MITS, SVCET, sathiyarajr@mits.ac.in, suji4068@gmail.com
Abstract
Research Scholars finds many problems in their Research and Development activities for the completion of their research work in
universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts
feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We
apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has
comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by
considering this constructed tree to improve their outcomes.
----------------------------------------------------------------------***-----------------------------------------------------------------------
1. INTRODUCTION
Data mining an interdisciplinary subfield of computer science,
is the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial
intelligence, machine learning, statistics, and database
systems. The overall goal of the data mining process is to
extract information from a data set and transform it into an
understandable structure for further use. The actual data
mining task is the automatic or semi-automatic analysis of
large quantities of data to extract previously unknown
interesting patterns such as groups of data records (cluster
analysis), unusual records (anomaly detection) and
dependencies (association rule mining). This usually involves
using database techniques such as spatial indices. These
patterns can then be seen as a kind of summary of the input
data, and may be used in further analysis or, for example,
in machine learning and analytics. Data Mining can be used to
solve many real time problems. Decision tree is an efficient
method that can be used in classification of data. A decision
tree is a decision support tool that uses a tree-like graph or
model of decisions and their possible consequences, including
chance event outcomes, resource costs, and utility. In this
paper, we use decision tree algorithm ID3 for analyzing
feedback given by guides. The training dataset consists of
attributes such as Research proposal, Qualification,
Experience, Way of Problem solving, Knowledge level,
Interaction with guide, Journals published, Implementation of
algorithm, Relating with real-life applications, Assessment,
Subject knowledge, Punctual and Nature. The outcomes in the
training dataset are specified with values like Excellent, Good,
Poor and Average. The ID3 Algorithm can be applied on this
training dataset to form a decision tree with view of guide as a
leaf node. Whenever any research scholars provide testing
data consisting of attribute values to the formed tree. Also, we
can suggest the possible area where he/she has scope for
improvement. This will help the scholar for self-evaluation
and improvement where they lag.
The Next section describes about the decision tree algorithm
and also defines entropy and gain ratio which are necessary
concepts for constructing decision tree using ID3 and the next
section by describing the problem statement and how we can
analyze the dataset and evaluate the problem by using ID3
algorithm; finally, the conclusions and future works are
outlined.
2. ID 3 ALGORITHM
A decision tree is a tree in which each branch node represents
a choice between a number of alternatives, and each leaf node
represents a decision. Decision tree are commonly used for
gaining information for the purpose of decision -making.
Decision tree starts with a root node on which it is for users to
take actions. From this node, users split each node recursively
according to decision tree learning algorithm. The final result
is a decision tree in which each branch represents a possible
scenario of decision and its outcome.
Decision tree learning is a method for approximating discrete-
valued target functions, in which the learned function is
represented by a decision tree.
ID3 is a simple decision learning algorithm developed by J.
Ross Quinlan (1986) at the University of Sydney. ID3 is based
off the Concept Learning System (CLS) algorithm. The basic
CLS algorithm over a set of training instances C:
Step 1: If all instances in C are positive, then create YES node
and halt.
If all instances in C are negative, create a NO node and halt.
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 10 | Oct-2013, Available @ http://www.ijret.org 381
Otherwise select a feature, F with values v1, ..., vn and create
a decision node.
Step 2: Partition the training instances in C into subsets C1,
C2, ..., Cn according to the values of V.
Step 3: apply the algorithm recursively to each of the sets Ci.
ID3 constructs decision tree by employing a top-down, greedy
search through the given sets of training data to test each
attribute at every node. It uses statistical property call
information gain to select which attribute to test at each node
in the tree. Information gain measures how well a given
attribute separates the training examples according to their
target classification. The algorithm uses a greedy search, that
is, it picks the best attribute and never looks back to reconsider
earlier choices.
2.1. Entropy
Entropy is a measure of the uncertainty in a random variable.
Entropy is typically measured in bits, nats, or bans. It is a
measure in the information theory, which characterizes the
impurity of an arbitrary collection of examples. If the target
attribute takes on c different values, then the entropy S relative
to this c-wise classification. Entropy is formally defined as
follows: If a data set S contains examples from m classes,
then the Entropy(S) is defined as following:
Where Pj is the probability of class j in S
Given a database state, D, Entropy (D) finds the amount of
order in that state. When that state is split into s new states S =
{D1, D2,…, Ds}, we can again look at the entropy of those
states. Each step in ID3 chooses the state that orders spitting
the most. A database state is completely ordered if all tuples in
it are in the same class.
2.2. Information Gain
ID3 chooses the splitting attribute with the highest gain in
information, where gain is defined as the difference between
how much information is needed to make a correct
classification before the split versus how much information is
needed after the split. Certainly, the split should reduce the
information needed by the largest amount. This is calculated
by determining the difference between the entropies of the
original dataset and the weighted sum of the entropies from
each of the subdivided datasets. The entropies of the split
datasets are weighted by the fraction of dataset being placed in
that division. The ID3 algorithm calculates the Information
Gain of a particular split by the following formula:
If attribute A is used to partition the data set S,
Where, v represents any possible values of attribute A;
Sv is the subset of S for which attribute A has value v;
|Sv| is the number of elements in Sv;
|S| is the number of elements in S.
ID3 Algorithm for Decision Tree can be given as
ID3 (Examples, Target_Attribute, Attributes)
1. Create a root node for the tree
2. IF all examples are positive, Return the single-node
tree Root, with label = +
3. If all examples are negative, Return the single-node
tree Root, with label = -
4. If number of predicting attributes is empty, then Return
the single node tree Root, with label = most common
value of the target attribute in the examples
5. Otherwise Begin
5.1 A The Attribute that best classifies examples
5.2 Decision Tree attribute for Root A
5.3 For each positive value, vi, of A,
5.3.1 Add a new tree branch below Root,
corresponding to the test A = vi
5.3.2 Let Examples (vi), be the subset of examples that
have the value vi for A
5.3.3 If Examples (vi) is empty
Then below this new branch add a leaf node
with label = most common target value in
the examples
Else below this new branch add the subtree
ID3 (Examples(vi), Target_Attribute,
Attributes – {A})
6. End
7. Return Root
The ID3 algorithm works by recursively applying the splitting
procedure to each of the subsets produced until “pure” nodes
are found—a pure node contains elements of only one class—
or until there are no attributes left to consider.
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Volume: 02 Issue: 10 | Oct-2013, Available @ http://www.ijret.org 382
3. DETERMINING IN-GENERAL-VIEW OF
GUIDES ABOUT THE SCHOLARS
The problem that we are considering here is to determine the
in-general-view of guides about the scholar. Based on the
outcome value, we can suggest ways for scholars to improve.
To achieve our goal, we use ID3 algorithm that is described in
the previous section. First, we can have the training dataset
containing following attributes:
Qualification, Experience, Way of Problem solving,
Knowledge level, Interaction with guide, Journals published,
Implementation of algorithm, Relating with real-life
applications, Assessment, Punctual and Nature
i. Qualification (GRADUATE,
POSTGRADUATE, DOCTORATE)
ii. Experience (LESS_THAN 2, 2-4, 4-8, 8-10, 10
ONWARDS)
iii. Way of Problem Solving (POOR, AVERAGE,
GOOD, EXCELLENT)
iv. Knowledge level (POOR, AVERAGE, GOOD
EXCELLENT)
v. Interaction with guide (POOR, AVERAGE
GOOD, EXCELLENT)
vi. Journals published (LESS_THAN 1, 2-4, 5
ONWARDS)
vii. Implementation of algorithm (YES, NO)
viii. Relating with real-life applications (YES, NO)
ix. Assessment (YES, NO)
x. Subject Knowledge (POOR, AVERAGE, GOOD,
EXCELLENT)
xi. Punctual (RARE, SOMETIMES, ALWAYS)
xii. Nature (COURTEOUS, RUDE,
INDIFFERENT)
The outcome class is: In-general-view (POOR,
GOOD, EXCELLENT). Here, we have converted the
continuous attributes to the discrete/categorical attributes by
considering the particular range as a class for simplicity and
applicability of ID3 algorithm.
Figure 1: Decision Tree
4. DETERMINING GUIDES VIEW AND
PROVIDING GUIDELINES TO SCHOLARS:
We solve the above mentioned problem using ID3 Algorithm.
To solve this, a decision tree is formed by classifying the
training data and then the outcome class value is determined.
The steps involved can be described as follows:
Decision Tree Construction: For each scholar registered in
university, we can have collective feedback for the attributes
enlisted in the problem statement. By using, ID3 algorithm, a
decision tree is formed by classifying the training data and
then the outcome class value is determined. The outcome class
will be the leaf node of the tree and the attribute values will be
the internal nodes and the arcs connecting the nodes are the
decision trees made during the decision tree construction.
Determination of in-general-view about scholar: If the
attribute values are provided, the decision tree formed after
classification can be used to determine the outcome class, by
traversing the tree using the attribute value. Scholars can
provide the attribute values to the constructed tree and obtain
outcome class value for self-evaluation.
Guidelines to the improvement of Scholars: Production
rules can be directly obtained by traversing from root to the
leves of the tree is the advantage of using decision tree. By
using the production rules, we can provide the guidelines for
the improvement of scholar.
For example: If a scholar gets ‘Poor’ as outcome class value
due to less value for some attribute(s), we can also give the
ways to get the outcome class value as ‘Excellent’, such as
values for Regularity attribute should be ALWAYS instead of
SOMETIMES, etc.
Thus the scholar can improve according to the guidelines. The
outcome will be more accurate, when the training set is larger.
If the training data set is too small, then it may not consider all
the possibilities for the particular outcome and the result may
not be accurate.
CONCLUSIONS AND FUTURE WORK
We conclude that ID3 Algorithm works well on classification
problems. In this paper, we use decision tree algorithm to
classify the dataset obtained from Guides feedback. We
determine guides in-general-view about scholars and also
provide guidelines to the scholars. This will be helpful for
scholars to evaluate themselves and to improve accordingly.
This will find its applicability in scholars’ assessment process.
In future, we are trying to implement with software tools and
we will assess the attribute values and calculate outcome class
by getting input values from professors in universities.
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 10 | Oct-2013, Available @ http://www.ijret.org 383
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[2] Anand Bahety,’ Extension and Evaluation of ID3 –
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[3] M. H. Dunham, DATA MINING: Introductory and
Advanced Topics, Pearson Education, Sixth Impression, 2009.
[4] P. Ozer, Data Mining Algorithms for Classification, BSc
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[5] A. Bahety, Extension and Evaluation of ID3 – Decision
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[6] J. R. Quinlan, Simplifying decision trees, International
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[7] P. H. Winston, Artificial Intelligence, Third Edition
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