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TAGA: Tabu Asexual Genetic Algorithm embedded in a filter/filter feature selection approach for high-dimensional data

journal contribution
posted on 05.02.2021, 11:31 by Sadegh Salesi, Georgina Cosma, Michalis Mavrovouniotis
Feature selection is the process of selecting an optimal subset of features required for maintaining or improving the performance of data mining models. Recently, hybrid filter/wrapper feature selection methods have shown promising results for high-dimensional data. However, filter/wrapper methods lack of generalisation power, which enables the selected features to be trainable over different classifiers without having to repeat the feature selection process. To address the generalisation power problem, this paper proposes a novel evolutionary-based filter feature selection algorithm that is sequentially hybridised with the Fisher score filter algorithm in a new hybrid framework called filter/filter. The proposed algorithm is based on a long-term memory Tabu Search combined with an Asexual (i.e. mutation-based) Genetic Algorithm (TAGA). TAGA benefits from a new integerencoded solution representation, a novel mutation operator, a new tabu list encoding scheme, and uses a minimum redundancy maximum relevance in formation theory-based criterion as the fitness function. Experiments were carried out on various high-dimensional datasets including image, text, and biological data. The goodness of the selected subsets was evaluated using different classifiers and the experimental results demonstrate that TAGA outperforms other conventional and state-of-the-art feature selection algorithms.

Funding

Leverhulme Trust Research Project Grant RPG-2016-252 entitled “Novel Approaches for Constructing Optimised Multimodal Data Spaces”

European Union’s Horizon 2020 research and innovation programme under grant agreement no. 739551 (KIOS CoE)

Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development

History

School

  • Science

Department

  • Computer Science

Published in

Information Sciences

Volume

565

Pages

105 - 127

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Information Sciences and the definitive published version is available at https://doi.org/10.1016/j.ins.2021.01.020.

Acceptance date

11/01/2021

Publication date

2021-01-26

Copyright date

2021

ISSN

0020-0255

Language

en

Depositor

Dr Georgina Cosma. Deposit date: 13 January 2021