Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm

Chengfeng Zheng, Mohd Shareduwan Mohd Kasihmuddin (), Mohd. Asyraf Mansor, Ju Chen and Yueling Guo
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Chengfeng Zheng: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Mohd Shareduwan Mohd Kasihmuddin: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Mohd. Asyraf Mansor: School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia
Ju Chen: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Yueling Guo: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia

Mathematics, 2022, vol. 10, issue 18, 1-23

Abstract: The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully studies the main parameters affecting the performance of the sine and cosine algorithm, integrates the reverse learning algorithm, adds an elite opposition solution and forms the hybrid sine and cosine algorithm (hybrid SCA). Combined with the fuzzy k-nearest neighbor method and the hybrid SCA, this paper numerically simulates two-class datasets and multi-class datasets, obtains a large number of numerical results and analyzes the results. The hybrid SCA FKNN proposed in this paper has achieved good accuracy in classification and prediction results under 10 different types of data sets. Compared with SCA FKNN, LSCA FKNN, BA FKNN, PSO FKNN and SSA FKNN, the prediction accuracy is significantly improved. In the Wilcoxon signed rank test with SCA FKNN and LSCA FKNN, the zero hypothesis (significance level 0.05) is rejected and the two classifiers have a significantly different accuracy.

Keywords: meta learning; data classification; hybrid sine and cosine algorithm; Wilcoxon signed rank test; multiple application scenario datasets (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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