Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/18/3368 |
| _version_ | 1797485123185147904 |
|---|---|
| author | Chengfeng Zheng Mohd Shareduwan Mohd Kasihmuddin Mohd. Asyraf Mansor Ju Chen Yueling Guo |
| author_facet | Chengfeng Zheng Mohd Shareduwan Mohd Kasihmuddin Mohd. Asyraf Mansor Ju Chen Yueling Guo |
| author_sort | Chengfeng Zheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:14:17Z |
| format | Article |
| id | doaj.art-04b3596fc4c3460f9f84411049189c92 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:14:17Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-04b3596fc4c3460f9f84411049189c922023-11-23T17:37:34ZengMDPI AGMathematics2227-73902022-09-011018336810.3390/math10183368Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine AlgorithmChengfeng Zheng0Mohd Shareduwan Mohd Kasihmuddin1Mohd. Asyraf Mansor2Ju Chen3Yueling Guo4School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Distance Education, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaThe 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.https://www.mdpi.com/2227-7390/10/18/3368meta learningdata classificationhybrid sine and cosine algorithmWilcoxon signed rank testmultiple application scenario datasets |
| spellingShingle | Chengfeng Zheng Mohd Shareduwan Mohd Kasihmuddin Mohd. Asyraf Mansor Ju Chen Yueling Guo Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm Mathematics meta learning data classification hybrid sine and cosine algorithm Wilcoxon signed rank test multiple application scenario datasets |
| title | Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm |
| title_full | Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm |
| title_fullStr | Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm |
| title_full_unstemmed | Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm |
| title_short | Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm |
| title_sort | intelligent multi strategy hybrid fuzzy k nearest neighbor using improved hybrid sine cosine algorithm |
| topic | meta learning data classification hybrid sine and cosine algorithm Wilcoxon signed rank test multiple application scenario datasets |
| url | https://www.mdpi.com/2227-7390/10/18/3368 |
| work_keys_str_mv | AT chengfengzheng intelligentmultistrategyhybridfuzzyknearestneighborusingimprovedhybridsinecosinealgorithm AT mohdshareduwanmohdkasihmuddin intelligentmultistrategyhybridfuzzyknearestneighborusingimprovedhybridsinecosinealgorithm AT mohdasyrafmansor intelligentmultistrategyhybridfuzzyknearestneighborusingimprovedhybridsinecosinealgorithm AT juchen intelligentmultistrategyhybridfuzzyknearestneighborusingimprovedhybridsinecosinealgorithm AT yuelingguo intelligentmultistrategyhybridfuzzyknearestneighborusingimprovedhybridsinecosinealgorithm |