# A Nature-Inspired Feature Selection Approach based on Hypercomplex Information

arXiv: https://arxiv.org/abs/2101.05652 · HTML: https://mlsanity.com/paper/2101.05652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization](https://mlsanity.com/paper/1408.5332/) (2014)

## Built on by (most cited first, 1 of 1)

- [Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization](https://mlsanity.com/paper/2301.13671/) (2023)

## Abstract (arXiv)

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.