Intelligent Hybrid Swarm based Feature Selection Methods using Rough Set
Tarun Maini1, Rakesh Kumar Misra2, Devender Singh3
1Tarun Maini, Research Scholar, Department of Electrical Engineering, IIT BHU, Varanasi (U.P), India.
2Rakesh Kumar Misra, Department of Electrical Engineering, IIT BHU, Varanasi (U.P), India.
3Devander Singh, Department of Electrical Engineering, IIT BHU, Varanasi (U.P), India.
Manuscript received on 16 November 2019 | Revised Manuscript received on 04 December 2019 | Manuscript Published on 10 December 2019 | PP: 157-163 | Volume-8 Issue-3S2 October 2019 | Retrieval Number: C10261083S219/2019©BEIESP | DOI: 10.35940/ijrte.C1026.1083S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: New feature selection methods based on Rough Set and hybrid optimization technique are proposed in this paper. In this work Feature Selection (Feature Reduction) has been implemented using Rough Set. Lower approximation based Rough Set has been used to calculate Positive Region which is consequently used to calculate Rough Dependency measure. Weighted sum of rough dependency measure and difference of total features of dataset and reduct normalized with respect to total feature, is used as fitness function. To optimize (maximize) this fitness function, a hybrid method of swarm intelligence algorithms like Intelligent Dynamic Swarm (IDS) and Particle Swarm Optimization (PSO) has been proposed and new method of population initialization has also been proposed. This method has been implemented on UCI repository based benchmark datasets of and it is shown that it results in improved reducts in terms of number of features, execution time with acceptable classification accuracy.
Keywords: Feature Selection, Rough Set, Swarm Intelligence, Intelligent Dynamic Swarm, Particle Swarm Optimization.
Scope of the Article: Evolutionary Computing and Intelligent Systems