A Novel Hybrid PSBCO Algorithm for Feature Selection

International Journal of Computer & Organization Trends  (IJCOT)          
© 2020 by IJCOT Journal
Volume - 10 Issue - 3
Year of Publication : 2020
Authors :  S.Sandhiya, Dr. U. Palani
DOI : 10.14445/22492593/IJCOT-V10I3P305


MLA Style:S.Sandhiya, Dr. U. Palani  "A Novel Hybrid PSBCO Algorithm for Feature Selection" International Journal of Computer and Organization Trends 10.3 (2020): 21-26. 

APA Style:S.Sandhiya, Dr. U. Palani(2020). A Novel Hybrid PSBCO Algorithm for Feature Selection International Journal of Computer and Organization Trends, 10(3), 21-26.


Feature selection is an important process in data mining which enriches the classification efficiency by eliminating irrelevant and redundant features from the original feature set. Feature selection plays a significant role in artificial intelligence and machine learning. In this paper, we propose a hybrid algorithm which combines the Binary cuckoo search algorithm (BCS) and Particle Swarm optimization (PSO) algorithm called Particle Swarm Binary Cuckoo optimization Algorithm (PSBCO). Proposed algorithm comprises of two steps, in first step subset generation is performed by using BCS optimization algorithm it will generate n number of subsets from the original large dataset. In second step subset selection is performed by PSO Algorithm it is used to evaluate all the selected subsets and selects the best subset from the generated n number of subset. PSBCO algorithm efficiency was tested with heart disease data using MOA tool. Through the experimental outcomes, PSBCO Algorithm has greater prediction accuracy with optimal number of selected features. The experimental results proves that the classification accuracy of proposed algorithm is significantly higher than the other classical cuckoo search and PSO algorithms.


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Feature selection, PSBCO, Binary Cuckoo Search, Particle Swarm.