Multi-Objective enhanced binary bat algorithm for unsupervised feature selection with classification validation
DOI:
https://doi.org/10.64497/jssci.269Keywords:
feature selection, binary bat algorithm, machine learning, optimization, crossoverAbstract
Feature selection is a vital preprocessing step for reducing dimensionality and improving model
generalization, especially in high-dimensional datasets. This paper presents a Multi-Objective Enhanced Binary
Bat Algorithm (MO-EBBA) for unsupervised feature selection, integrating a novel crossover operator and
adaptive pulse rate mechanism. Unlike traditional single-objective approaches, MO-EBBA simultaneously
optimizes two conflicting objectives: maximizing clustering quality (measured via Silhouette Coefficient) and
minimizing the number of selected features. The selected features are further validated through multiple
classifiers (SVM, k-NN, Decision Tree, and Random Forest), using supervised metrics such as accuracy, F1-
score, precision, and recall. We conduct experiments on several benchmark datasets, demonstrating that MO-
EBBA achieves superior trade-offs between feature reduction and classification performance compared to
existing state-of-the-art methods. Also, we conducted statistical tests to confirm the significance of the
improvements of the new method.
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Copyright (c) 2026 Alhassan Adamu, Isah Tajiri Sagagi, Salisu Mamman Abdulrahman, Umma Abubakar, Bashir Salisu Abubakar, Ali Olow Jimale
This work is licensed under a Creative Commons Attribution 4.0 International License.
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