Swarm intelligence-based feature selection for amphetamine-type stimulants (ATS) drug 3D molecular structure classification

• Main Authors: Draman @ Muda, Azah Kamilah, Mohd Yusof, Norfadzlia, Pratama, Satrya Fajri
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021
• Online Access: http://eprints.utem.edu.my/id/eprint/26820/2/08839514.2021.PDF; http://eprints.utem.edu.my/id/eprint/26820/; https://www.tandfonline.com/doi/epdf/10.1080/08839514.2021.1966882?needAccess=true&role=button; https://doi.org/10.1080/08839514.2021.1966882
• Institution: Universiti Teknikal Malaysia Melaka

Swarm intelligence-based feature selection techniques are implemented by this work to increase classifier performance in classifying Amphetamine-type Stimulants (ATS) drugs. A recently proposed 3D Exact Legendre Moment Invariants (3D- ELMI) molecular descriptors as 3D molecular structure represen-tational for ATS drugs. These descriptors are utilized as the dataset in this study. However, a large number of descriptors may cause performance degradation in the classifier. To com-plement this issue, this research applies three swarm algorithms with k-Nearest Neighbor (k-NN) classifier in the wrapper feature selection technique to ensure only relevant descriptors are selected for the ATS drug classification task. For this purpose, the binary version of swarm algorithms facilitated with the S-shaped or sigmoid transfer function known as binary whale optimization algorithm (BWOA), binary particle swarm optimiza-tion algorithm (BPSO), and new binary manta-ray foraging opti-mization algorithm (BMRFO) are developed for feature selection. Their performance is evaluated and compared based on seven performance criteria. Furthermore, the optimal feature subset was then evaluated with seven different classifiers. Findings from this study have revealed the dominance of BWOA by obtaining the highest classification accuracy with the small feature size.