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Memetic Computing
Published in: Memetic Computing 3/2018

07-03-2018 | Regular Research Paper

PSO with surrogate models for feature selection: static and dynamic clustering-based methods

Authors: Hoai Bach Nguyen, Bing Xue, Peter Andreae

Published in: Memetic Computing | Issue 3/2018

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Abstract

Feature selection is an important but often expensive process, especially with a large number of instances. This problem can be addressed by using a small training set, i.e. a surrogate set. In this work, we propose to use a hierarchical clustering method to build various surrogate sets, which allows to analyze the effect of surrogate sets with different qualities and quantities on the feature subsets. Further, a dynamic surrogate model is proposed to automatically adjust surrogate sets for different datasets. Based on this idea, a feature selection system is developed using particle swarm optimization as the search mechanism. The experiments show that the hierarchical clustering method can build better surrogate sets to reduce the computational time, improve the feature selection performance, and alleviate overfitting. The dynamic method can automatically choose suitable surrogate sets to further improve the classification accuracy.
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Metadata
Title
PSO with surrogate models for feature selection: static and dynamic clustering-based methods
Authors
Hoai Bach Nguyen
Bing Xue
Peter Andreae
Publication date
07-03-2018
Publisher
Springer Berlin Heidelberg
Published in
Memetic Computing / Issue 3/2018
Print ISSN: 1865-9284
Electronic ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-018-0254-9

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