Improving Amphetamine-type Stimulants drug classification using chaotic-based time-varying binary whale optimization algorithm - Mohd Yusof, Norfadzlia; Muda, Azah Kamilah; Pratama, Satrya Fajri; Carbo-Dorca, Ramon; Abraham, Ajith (Chemometrics and intelligent laboratory systems, 2022)
A new chaotic time-varying binary whale optimization algorithm (CBWOATV) is introduced in this paper to optimize the feature selection process in Amphetamine-type Stimulants (ATS) and non-ATS drugs classification. Two enhancement methods were introduced in this study to provide a fit balance between exploration and exploitation in standard WOA. Firstly, a non-linear time-varying modified Sigmoid transfer function is used as the binarization method. Second, a hybrid Logistic-Tent chaotic map is employed to substitute the pseudorandom numbers of the probability operator in standard WOA. Specific high-dimensional molecular descriptors of ATS and non-ATS drugs were employed to evaluate the efficiency of the proposed algorithm. Experimental results and statistical analysis indicate that the proposed CBWOATV algorithm can prevent the problem of stagnation and entrapment in local minima in WOA. As a result, optimal descriptors were selected contributing to enhanced classification performance. •This study proposed CBWOATV2 algorithm that implement a time-varying modified Sigmoid transfer function and Logistic-tent chaotic map.•CBWOATV2 algorithm is integrated with wrapper feature selection technique to solve descriptor selection problem in ATS drug analysis.•The proposed CBWOATV2 algorithm is compared with six state-of-the-art binary SI-based feature selection algorithms: BWOA, CWOA-PI, BWOA-TV2, MRFOv3, TVT-BPSO, and BHHOTV4.•Experimental results and statistical analysis confirmed the significant improvements by CBWOATV2 compared to the comparing algorithms.