Instigating the Sailfish Optimization Algorithm Based on Opposition-Based Learning to Determine the Salient Features From a High-Dimensional Dataset

Utkarsh Mahadeo Khaire, R. Dhanalakshmi, K. Balakrishnan and M. Akila
Additional contact information
Utkarsh Mahadeo Khaire: Department of Data Science and Intelligent Systems, Indian Institute of Information Technology Dharwad, Karnataka 580009, India
R. Dhanalakshmi: ��Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tamil Nadu 620012, India
K. Balakrishnan: ��Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tamil Nadu 620012, India
M. Akila: ��Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu 641407, India

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 05, 1617-1649

Abstract: The aim of this research critique is to propose a hybrid combination of Opposition-Based Learning and Sailfish Optimization strategy to recognize the salient features from a high-dimensional dataset. The Sailfish Optimization is a swarm-based metaheuristics optimization algorithm inspired by the foraging strategy of a group of Sailfish. Sailfish Optimization explores the search space in only one direction, limiting its converging capacity and causing local minima stagnation. Convergence will be optimal if the search space is reconnoitred in both directions, improving classification accuracy. As a result, combining the Opposition-Based Learning and Sailfish Optimization strategies improves SFO’s exploration capability by patrolling the search space in all directions. Sailfish Optimization Algorithm based on Opposition-Based Learning successfully amalgamates the model to global optima at a faster convergence rate and better classification accuracy. The recommended method is tested with six different cancer microarray datasets for two different classifiers: the Support Vector Machine classifier and the K-Nearest Neighbor classifier. From the results obtained, the proposed model aided with Support Vector Machine outperforms the existing Sailfish Optimization with or without K-Nearest Neighbor in terms of convergence capability, classification accuracy, and selection of the most delicate salient features from the dataset.

Keywords: Feature selection; Opposition-based Learning; Sailfish Optimization; metaheuristic optimization; microarray dataset (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622022500754
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:22:y:2023:i:05:n:s0219622022500754

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622022500754

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().