A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification

TL;DR

This paper introduces a tribe competition-based genetic algorithm (TCbGA) that improves feature selection for pattern classification by avoiding bias and pre-specified feature counts, leading to more accurate results.

Contribution

The paper proposes a novel TCbGA method that uses tribe competition and Gaussian distribution-based exploration to enhance feature selection without bias or preset feature numbers.

Findings

Outperforms several state-of-the-art methods on 20 benchmark datasets

More effective in identifying optimal feature subsets

Produces more accurate pattern classification results

Abstract

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.