.png){kind=icon}
.png){kind=icon}
Return
Improving Amphetamine-type Stimulants drug classification using chaotic-based time-varying binary whale optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2022.104635.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Drug classification
Descriptors selection
Binary whale optimization algorithm
Time -varying transfer function
Logistic -tent chaotic map
Journal
IF:
3.8
Papers:
4.6K
Citations:
1.2W