BMPA-TVSinV: A binary metaheuristic for Feature Selection

This folder contains the code of Binary Marine Predator using Time-Varying Sinus & V-shaped transfer functions for Feature Selection.

The feature selection problem is one of the pre-processing mechanisms to find the optimal subset of features from a dataset. The search space of the problem will exponentially grow when the number of features increases. Hence, the feature selection problem is classified as an NP-hard problem, and exact algorithms cannot find the optimal subset at a reasonable time. As a result, approximate algorithms like meta-heuristic algorithms are extensively applied to solve the problem. The feature selection problem is a discrete (binary) optimization problem; consequently, a discrete meta-algorithm can be employed to find the optimal subset of features. One of the recently introduced meta-heuristic algorithms is Marine Predator Algorithm (MPA), which has shown good solutions to many continuous optimization problems. A novel Binary Marine Predator Algorithm using Time-Varying Sinus and V-shaped transfer functions (BMPA-TVSinV) is introduced to find the optimal subset of features in datasets. The proposed algorithm applies two new time-varying transfer functions to convert the continuous search space to the binary one. These transfer functions considerably improve the performance of BMPA-TVSinV for feature selection. A COVID-19 dataset is used to show the efficiency of BMPA-TVSinV.in the problem.