A modified fuzzy K-nearest neighbor using sine cosine algorithm for two-classes and multi-classes datasets

The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented...

Full description

Saved in:
Bibliographic Details
Main Authors: Zheng, Chengfeng, Kasihmuddin, Mohd Shareduwan Mohd, Mansor, Mohd. Asyraf, Jamaludin, Siti Zulaikha Mohd, Zamri, Nur Ezlin
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Datasets
Machine learning
Parameter modification
Trigonometric functions
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented modifications such as integrating the reverse learning algorithm and adding elite opposition solution to create the modified Sine and Cosine Algorithm (the modified SCA). Furthermore, by combining the fuzzy k-nearest neighbor method with the modified SCA, the study simulates numeric datasets with two or multiple classes, and analyzes the results. The accuracy rate (ACC) achieved by the modified SCA FKNN in this paper is compared to other models, with data comparison results and tables presented for each. The modified SCA FKNN proposed in this paper has obvious advantages on accuracy rate(ACC).
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0192167