A Nature-Inspired Feature Selection Approach based on Hypercomplex Information

TL;DR

This paper introduces a novel hypercomplex-based meta-heuristic feature selection method that leverages mathematical representations like quaternions to improve selection efficiency, demonstrating competitive results with state-of-the-art techniques.

Contribution

It presents a new hypercomplex optimization framework for feature selection, combining hypercomplex numbers with meta-heuristics to handle high-dimensional spaces effectively.

Findings

Achieved results comparable to state-of-the-art methods

Demonstrated effectiveness across various meta-heuristic algorithms

Proved the approach's potential as a promising feature selection tool

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.