A Novel Memetic Feature Selection Algorithm

TL;DR

This paper introduces a memetic feature selection algorithm that combines filter methods with genetic algorithms to efficiently identify feature subsets, improving classification accuracy and reducing complexity.

Contribution

The paper proposes a novel memetic algorithm integrating filter methods with genetic algorithms for enhanced feature selection performance.

Findings

Outperforms existing feature selection methods on UCI datasets.

Accelerates search for core feature subsets.

Improves classification accuracy with fewer features.

Abstract

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods.