BMPA-TVSinV: A binary metaheuristic for Feature Selection

版本 1.0.0 (31.0 KB) 作者: Zahra Beheshti
This folder contains the code of Binary Marine Predator using Time-Varying Sinus & V-shaped transfer functions for Feature Selection.
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更新时间 2022/7/21

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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.

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Zahra Beheshti (2024). BMPA-TVSinV: A binary metaheuristic for Feature Selection (https://www.mathworks.com/matlabcentral/fileexchange/115315-bmpa-tvsinv-a-binary-metaheuristic-for-feature-selection), MATLAB Central File Exchange. 检索时间: .

Z. Beheshti, BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sinus and V-shaped transfer functions for wrapper-based feature selection, Knowledge-Based Systems (2022) 109446. doi:https://doi.org/10.1016/j.knosys.2022.109446.

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BMPA-TVSinV

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1.0.0