Artificial Bee Colony Approach for Optimizing Feature Selection
High dimensionality of the feature space affects the classification accuracies and the computational complexity due to redundant, irrelevant and noisy features present in the dataset. Feature Selection extracts the relevant and most useful information and helps to speed up the task of classification. Feature selection is seen as an optimization problem because selecting the appropriate, optimal feature subset is very important. The Artificial Bee Colony algorithm is a famous meta-heuristic search algorithm used in solving combinatorial optimization problems. This paper proposes a new method of feature selection, which uses the ABC algorithm to optimize the selection of features. Ten UCI datasets have been used for evaluating the proposed algorithm. Experimental results show that, ABC-Feature Selection has resulted in optimal feature subset configuration and increased classification accuracies up to 12% compared to the classifier and standard ensembles.
Keywords: Classification, Feature Selection, Ant Colony Optimization, Bee Colony Optimization, Artificial Bee Colony Algorithm.
Download Full-Text
ABOUT THE AUTHORS
Shunmugapriya Palanisamy
P.Shunmugapriya received her M.E. degree in 2006 from Department of Computer Science and Engineering, FEAT, Annamalai University, Chidambaram. She had been working as a Senior Lecturer for the past 7 years in the Department of Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Affiliated to Pondicherry University, Puducherry. Currently she is working towards her Ph.D degree in Optimal Design of Classifier Ensembles using Swarm Intelligent, Meta-Heuristic Search Algorithms. Her areas of interest are Artificial Intelligence, Ontology based Software Engineering, Classifier Ensembles and Swarm Intelligence.
Kanmani S
Dr. S. Kanmani received her B.E and M.E in Computer Science and Engineering from Bharathiyar University and Ph.D in Anna University, Chennai. She had been the faculty of Department of Computer Science and Engineering, Pondicherry Engineering College from 1992 onwards. Presently she is a Professor in the Department of Information Technology, Pondicherry Engineering College, Puducherry. Her research interests are Software Engineering, Software testing, Object oriented system, and Data Mining. She is Member of Computer Society of India, ISTE and Institute of Engineers, India. She has published about 50 papers in various international conferences and journals.
Shunmugapriya Palanisamy
P.Shunmugapriya received her M.E. degree in 2006 from Department of Computer Science and Engineering, FEAT, Annamalai University, Chidambaram. She had been working as a Senior Lecturer for the past 7 years in the Department of Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Affiliated to Pondicherry University, Puducherry. Currently she is working towards her Ph.D degree in Optimal Design of Classifier Ensembles using Swarm Intelligent, Meta-Heuristic Search Algorithms. Her areas of interest are Artificial Intelligence, Ontology based Software Engineering, Classifier Ensembles and Swarm Intelligence.
Kanmani S
Dr. S. Kanmani received her B.E and M.E in Computer Science and Engineering from Bharathiyar University and Ph.D in Anna University, Chennai. She had been the faculty of Department of Computer Science and Engineering, Pondicherry Engineering College from 1992 onwards. Presently she is a Professor in the Department of Information Technology, Pondicherry Engineering College, Puducherry. Her research interests are Software Engineering, Software testing, Object oriented system, and Data Mining. She is Member of Computer Society of India, ISTE and Institute of Engineers, India. She has published about 50 papers in various international conferences and journals.
