A nature-inspired feature selection approach based on hypercomplex information

Author(s)

de Rosa, Gustavo H. ^[1] ; Papa, Joao P. ^[1] ; Yang, Xin-She ^[2]

Affiliation

^[1] Sao Paulo State Univ, Dept Comp, Ave Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil

^[2] Middlesex Univ, Sch Sci & Technol, London NW4 4BT - England

• Document type: Journal article
• Source: APPLIED SOFT COMPUTING; v. 94, SEP 2020.
• Web of Science Citations: 0

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. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
• Grantee: João Paulo Papa
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 17/02286-0 - Probabilistic models for commercial losses detection
• Grantee: André Nunes de Souza
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/02205-5 - Adversarial learning in natural language processing
• Grantee: Gustavo Henrique de Rosa
• Support Opportunities: Scholarships in Brazil - Doctorate