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Adaptive multi-subswarm optimisation for feature selection on high-dimensional classification

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conference contribution
posted on 18.01.2021, 05:46 by Binh TranBinh Tran, B Xue, M Zhang
Feature space is an important factor influencing the performance of any machine learning algorithm including classification methods. Feature selection aims to remove irrelevant and redundant features that may negatively affect the learning process especially on high-dimensional data, which usually suffers from the curse of dimensionality. Feature ranking is one of the most scalable feature selection approaches to high-dimensional problems, but most of them fail to automatically determine the number of selected features as well as detect redundancy between features. Particle swarm optimisation (PSO) is a population-based algorithm which has shown to be effective in addressing these limitations. However, its performance on high-dimensional data is still limited due to the large search space and high computation cost. This study proposes the first adaptive multi-swarm optimisation (AMSO) method for feature selection that can automatically select a feature subset of high-dimensional data more effectively and efficiently than the compared methods. The subswarms are automatically and dynamically changed based on their performance during the evolutionary process. Experiments on ten high-dimensional datasets of varying difficulties have shown that AMSO is more effective and more efficient than the compared PSO-based and traditional feature selection methods in most cases.

History

Publication Date

01/01/2019

Proceedings

GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference

Editors

Lopez-Ibanez M

Publisher

Association for Computing Machinery

Place of publication

New York

Pagination

9p. (p. 481-489)

ISBN-13

9781450361118

Name of conference

GECCO: The Genetic and Evolutionary Computation Conference

Location

Prague, Czech Republic

Starting Date

13/07/2019

Finshing Date

17/07/2019

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.