A hybrid search method of wrapper feature selection by chaos particle swarm optimization and local search

Authors

MOHAMMAD MASOUD JAVIDI
Nasibeh EMAMI

DOI

10.3906/elk-1404-220

Abstract

Finding a subset of features from a large dataset is a problem that arises in many fields of study. Since the increasing number of features has extended the computational cost of a system, it is necessary to design and implement a system with the least number of features. The purpose of feature selection is to find the best subset of features from the original ones. The result of the best selection is improving the computational cost and the accuracy of the prediction. A large number of algorithms have been proposed for feature subset selection. In this paper, we propose a wrapper feature selection algorithm for a classification that is based on chaos theory, binary particle swarm optimization, and local search. In the proposed algorithm, the nearest neighbor algorithm is used for the evaluation phase.

Keywords

Feature selection problem, metaheuristic approach, local search, binary particle swarm optimization, chaos theory

First Page

3852

Last Page

3861

Recommended Citation

JAVIDI, MOHAMMAD MASOUD and EMAMI, Nasibeh (2016) "A hybrid search method of wrapper feature selection by chaos particle swarm optimization and local search," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 24: No. 5, Article 40. https://doi.org/10.3906/elk-1404-220
Available at: https://journals.tubitak.gov.tr/elektrik/vol24/iss5/40