Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification
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 representati...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-10-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1966882 |
| _version_ | 1797684846186725376 |
|---|---|
| author | Norfadzlia Mohd Yusof Azah Kamilah Muda Satrya Fajri Pratama |
| author_facet | Norfadzlia Mohd Yusof Azah Kamilah Muda Satrya Fajri Pratama |
| author_sort | Norfadzlia Mohd Yusof |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:36:43Z |
| format | Article |
| id | doaj.art-f368671f02374415b07ae57c1ee6818b |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:43Z |
| publishDate | 2021-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-f368671f02374415b07ae57c1ee6818b2023-09-15T09:33:59ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452021-10-01351291493210.1080/08839514.2021.19668821966882Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure ClassificationNorfadzlia Mohd Yusof0Azah Kamilah Muda1Satrya Fajri Pratama2Universiti Teknikal Malaysia Melaka, Hang Tuah JayaUniversiti Teknikal Malaysia Melaka, Hang Tuah JayaUniversiti Teknikal Malaysia Melaka, Hang Tuah JayaSwarm 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.http://dx.doi.org/10.1080/08839514.2021.1966882 |
| spellingShingle | Norfadzlia Mohd Yusof Azah Kamilah Muda Satrya Fajri Pratama Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification Applied Artificial Intelligence |
| title | Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification |
| title_full | Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification |
| title_fullStr | Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification |
| title_full_unstemmed | Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification |
| title_short | Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification |
| title_sort | swarm intelligence based feature selection for amphetamine type stimulants ats drug 3d molecular structure classification |
| url | http://dx.doi.org/10.1080/08839514.2021.1966882 |
| work_keys_str_mv | AT norfadzliamohdyusof swarmintelligencebasedfeatureselectionforamphetaminetypestimulantsatsdrug3dmolecularstructureclassification AT azahkamilahmuda swarmintelligencebasedfeatureselectionforamphetaminetypestimulantsatsdrug3dmolecularstructureclassification AT satryafajripratama swarmintelligencebasedfeatureselectionforamphetaminetypestimulantsatsdrug3dmolecularstructureclassification |