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

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conference contribution
posted on 2021-01-18, 05:46 authored 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.


Publication Date



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


Lopez-Ibanez M


Association for Computing Machinery

Place of publication

New York


9p. (p. 481-489)



Name of conference

GECCO: The Genetic and Evolutionary Computation Conference


Prague, Czech Republic

Starting Date


Finshing Date


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