  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.
