Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification

Figures & data

Figure 1. Principal steps of molecular descriptors generation for computational models

Figure 2. Process flow of the proposed ATS drug classification system

Table 1. Attributes description

Attribute	Attribute type	No. of attribute	Description
Molecule id	String	1	Molecule id refers to the reference id of the drug molecule in the original database.
Featuren	Numeric (real numbers)	1185	Moment invariants value calculated using 3D-ELMI molecular descriptors to represent the drug 3D molecular structure. n is feature number takes value from 1 to 1185.
Class	Nominal {0,1}	1	0: non-ATS and 1: ATS

Table 2. BPSO, BWOA, and BMRFO parameters setting

Parameter	Value
BPSO, BWOA, BMRFO
No. of runs, M	15
Problem dimension, d	1185
No. of search agents, n	5
No. of iterations, t	70
$\alpha$ parameter in the fitness function	0.99
$\beta$ parameter in the fitness function	0.01
BPSO
Cognitive factor, c1	2
Social factor, c2	2
Maximum velocity, Vmax	6
Minimum velocity, Vmin	−6
Inertia weight, w	0.9 to 0.4
BWOA
$\vec{a}$ in WOA	2 to 0
b	1
BMRFO
Somersault factor, S	2

Table 3. Results show the mean of minimum fitness (Min), maximum fitness (Max), mean fitness (Mean), standard deviation (Std), and computation time (CT) obtained by BPSO, BWOA, and BMRFO algorithms

Algorithm	Min	Max	Mean	Std	CT
BPSO	0.18451	0.26298	0.19746	0.01585	34.51
BWOA	0.18650	0.23819	0.19535	0.01152	402.51
BMRFO	0.18997	0.24396	0.19536	0.00945	1015.03

Figure 3. Convergence curves of BPSO, BWOA, and BMRFO algorithms

Table 4. Results show the means of accuracy an selected feature size of BPSO, BWOA and BMRFO algorithms

Algorithm	Mean Accuracy (%)	Mean Selected Feature Size
All	62.63%	1185
BPSO	81.39%	37
BWOA	81.41%	293.3
BMRFO	81.11%	350.15

Table 5. Mean classification accuracies with different classifiers

Classifier	All	BPSO	BWOA	BMRFO
k-NN	62.63%	81.39%	81.41%	81.11%
SVM (linear)	77.98%	78.62%	79.83%	79.88%
SVM (RBF)	67.24%	80.93%	82.00%	82.02%
SVM (Polynomial)	50.29%	66.40%	79.51%	79.75%
SVM(Gaussian)	67.46%	80.95%	81.99%	82.00%
NB (Normal)	68.37%	71.15%	68.57%	68.23%
NB (Kernel)	66.53%	68.77%	66.52%	66.73%

Table 6. Mean classification time in seconds with different classifiers

Classifier	All	BPSO	BWOA	BMRFO
k-NN	3.12	0.07	0.69	0.82
SVM (linear)	64.44	1.91	3.43	3.94
SVM (RBF)	22.40	0.97	3.56	4.18
SVM (Polynomial)	40.96	69.03	69.90	74.84
SVM(Gaussian)	22.00	0.95	3.61	4.27
NB (Normal)	0.39	0.02	0.10	0.10
NB (Kernel)	98.01	2.92	23.49	27.16

Figure 4. The average mean classification accuracies and times by all classifiers