A Survey on Evolutionary Computation Approaches to Feature Selection

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 me...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 20; no. 4; pp. 606 - 626
Main Authors: Bing Xue, Mengjie Zhang, Browne, Will N., Xin Yao
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
Computational efficiency
Data mining
Drafting
Evolutionary algorithms
Evolutionary computation
Feature extraction
feature selection
Guidelines
Machine learning
Machine learning algorithms
Optimization
Production planning
Search problems
Searching
State of the art
Tasks
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2504420