TY - GEN
T1 - Binary Whale Optimization Algorithm with Logarithmic Decreasing Time-Varying Modified Sigmoid Transfer Function for Descriptor Selection Problem
AU - Yusof, Norfadzlia Mohd
AU - Muda, Azah Kamilah
AU - Pratama, Satrya Fajri
AU - Carbo-Dorca, Ramon
AU - Abraham, Ajith
N1 - © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/3/28
Y1 - 2023/3/28
N2 - In cheminformatics, choosing the right descriptors is a crucial step in improving predictive models, particularly those that use machine learning algorithms. Recently, researchers in cheminformatics have been lured to swarm intelligence to optimize the process of discovering relevant descriptors in the wrapper feature selection. This work introduced a new Binary Whale Optimization Algorithm, which utilized a novel time-varying modified Sigmoid transfer function with a modified logarithmic decreasing time-varying update strategy to improve the balancing of exploration and exploitation in WOA. The new Binary Whale Optimization Algorithm is integrated with wrapper feature selection and validated on descriptor selection problem to improve Amphetamine-type stimulants drug classification result. The suggested approach is compared to well-known swarm intelligence algorithms, and the results demonstrate its superiority.
AB - In cheminformatics, choosing the right descriptors is a crucial step in improving predictive models, particularly those that use machine learning algorithms. Recently, researchers in cheminformatics have been lured to swarm intelligence to optimize the process of discovering relevant descriptors in the wrapper feature selection. This work introduced a new Binary Whale Optimization Algorithm, which utilized a novel time-varying modified Sigmoid transfer function with a modified logarithmic decreasing time-varying update strategy to improve the balancing of exploration and exploitation in WOA. The new Binary Whale Optimization Algorithm is integrated with wrapper feature selection and validated on descriptor selection problem to improve Amphetamine-type stimulants drug classification result. The suggested approach is compared to well-known swarm intelligence algorithms, and the results demonstrate its superiority.
KW - ATS drug classification
KW - Binary whale optimization algorithm
KW - Modified logarithmic decreasing time-varying update technique
KW - Time-varying transfer function
UR - http://www.scopus.com/inward/record.url?scp=85152548682&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-27524-1_65
DO - 10.1007/978-3-031-27524-1_65
M3 - Conference contribution
AN - SCOPUS:85152548682
SN - 9783031275234
T3 - Lecture Notes in Networks and Systems
SP - 673
EP - 681
BT - Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022
A2 - Abraham, Ajith
A2 - Abraham, Ajith
A2 - Gandhi, Niketa
A2 - Hanne, Thomas
A2 - Manghirmalani Mishra, Pooja
A2 - Bajaj, Anu
A2 - Siarry, Patrick
PB - Springer Nature Link
T2 - 14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022
Y2 - 14 December 2022 through 16 December 2022
ER -