A nature-inspired feature selection approach based on hypercomplex information

de Rosa, Gustavo H. ORCID logoORCID: https://orcid.org/0000-0002-6442-8343, Papa, João P. and Yang, Xin-She ORCID logoORCID: https://orcid.org/0000-0001-8231-5556 (2020) A nature-inspired feature selection approach based on hypercomplex information. Applied Soft Computing, 94 , 106453. ISSN 1568-4946 [Article] (doi:10.1016/j.asoc.2020.106453)

[img]
Preview
PDF - Final accepted version (with author's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0.

Download (4MB) | Preview

Abstract

Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 30351
Notes on copyright: © 2020. This author's accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 12 Jun 2020 07:30
Last Modified: 25 Jun 2022 17:56
URI: https://eprints.mdx.ac.uk/id/eprint/30351

Actions (login required)

View Item View Item

Statistics

Activity Overview
6 month trend
42Downloads
6 month trend
95Hits

Additional statistics are available via IRStats2.