TY - JOUR
T1 - Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification
AU - Yusof, Norfadzlia Mohd
AU - Muda, Azah Kamilah
AU - Pratama, Satrya Fajri
N1 - Publisher Copyright:
© 2021 Taylor & Francis.
PY - 2021
Y1 - 2021
N2 - 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 representational 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 complement 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 optimization algorithm (BPSO), and new binary manta-ray foraging optimization 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.
AB - 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 representational 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 complement 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 optimization algorithm (BPSO), and new binary manta-ray foraging optimization 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.
UR - http://www.scopus.com/inward/record.url?scp=85113754506&partnerID=8YFLogxK
U2 - 10.1080/08839514.2021.1966882
DO - 10.1080/08839514.2021.1966882
M3 - Article
AN - SCOPUS:85113754506
SN - 0883-9514
VL - 35
SP - 914
EP - 932
JO - Applied Artificial Intelligence
JF - Applied Artificial Intelligence
IS - 12
ER -