Binary Adaptive Quantum-Inspired Optimizer : A Novel Wrapper Feature Selection Approach with Lévy-Guided Collapse and Opposition-Based Initialization
Keywords:
Feature selection ;Binary metaheuristic;Quantum-inspired optimization;Opposition-based learning ; Lévy flight ; Wrapper method;UCI benchmarks; ClassificationAbstract
Feature selection is a critical preprocessing step in knowledge-based systems and machine learning pipelines, directly affecting model accuracy, interpretability, and computational efficiency. This paper proposes BAQIO, a Binary Adaptive Quantum-Inspired Optimizer, a novel wrapper-based feature selection algorithm that addresses three limitations prevalent in existing binary metaheuristics: loss of population diversity caused by rapid convergence, inadequate exploration of high-dimensional discrete search spaces, and sensitivity to initialization quality. BAQIO encodes each feature subset as a quantum-bit string, where each bit is represented by a rotation angle that probabilistically collapses to a binary observation. Three cooperative mechanisms drive the search: (i) an adaptive rotation angle update that modulates the quantum collapse intensity based on the fitness difference between each individual and the global best; (ii) Lévy-guided stochastic jumps applied to rotation angles of the elite sub-population to enable wide-range exploration; and (iii) opposition-based initialization, which initializes both the original and complementary quantum states and retains the better half, doubling the effective coverage of the binary feature space. BAQIO is evaluated on 18 standard UCI datasets spanning binary and multi-class problems, low- and high-dimensional feature spaces (9 to 12,533 features), and varying sample sizes (32 to 5,000 instances). It is benchmarked against eight state-of-the-art binary optimizers — BPSO, BGWO, BWOA, BSSA, BMPA, BHHO, BSCA, and BARO — using KNN (k = 5) as the classifier. BAQIO achieves the highest classification accuracy on 16 of 18 datasets and the smallest selected feature subset on all 18. Wilcoxon signed-rank tests with Bonferroni correction confirm that BAQIO's improvements are statistically significant against all eight competitors on at least 14 of 18 datasets (α = 0.05).
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