A Survey on Evolutionary Computation Approaches to Feature Selection

TLDR

Feature selection reduces dimensionality and improves algorithm performance, yet its large search space makes it challenging; evolutionary computation methods have attracted attention, but the field lacks comprehensive guidelines, leading to fragmented research. The paper surveys state‑of‑the‑art evolutionary computation approaches to feature selection, identifying their contributions and discussing current issues and challenges to highlight promising future research directions. The authors conduct a comprehensive review of evolutionary computation techniques for feature selection, cataloguing their contributions and analyzing current challenges to inform future work.

Abstract

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.