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A Survey on Evolutionary Computation Approaches to Feature Selection
Open accessAbstract
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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Cites
An Introduction to Genetic Algorithms
Mitchell, M. (1996) · doi:10.7551/mitpress/3927.001.0001 · Cited by 11,340
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Metadata
- Authors
- Xue, B., Zhang, M., Browne, W. N., & Yao, X.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2016-08
- Volume
- 20
- Issue
- 4
- Pages
- 606-626
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2343420905
- Cited by
- 1,841
- References
- 1
- FWCI
- 80.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2016). A Survey on Evolutionary Computation Approaches to Feature Selection. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2015.2504420
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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