A multi-voter multi-commission nearest neighbor classifier
Many improved versions of k-nearest neighbor (KNN) have been proposed by minimizing total distances of multi k nearest neighbors (multi-voter) in each class instead of the majority voting, such as a local mean-based pseudo nearest neighbor (LMPNN) that give a better decision. In this paper, a new KN...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822000313 |
| _version_ | 1811281010987368448 |
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| author | Suyanto Suyanto Prasti Eko Yunanto Tenia Wahyuningrum Siti Khomsah |
| author_facet | Suyanto Suyanto Prasti Eko Yunanto Tenia Wahyuningrum Siti Khomsah |
| author_sort | Suyanto Suyanto |
| collection | DOAJ |
| description | Many improved versions of k-nearest neighbor (KNN) have been proposed by minimizing total distances of multi k nearest neighbors (multi-voter) in each class instead of the majority voting, such as a local mean-based pseudo nearest neighbor (LMPNN) that give a better decision. In this paper, a new KNN variant called multi-voter multi-commission nearest neighbor (MVMCNN) is proposed to examine its benefits in enhancing the LMPNN. As the name suggests, MVMCNN uses some commissions: each calculates the total distance between the given query point (test pattern) and k pseudo nearest neighbors using the LMPNN scheme. The decision class is defined by minimizing those total distances. Hence, the decision in MVMCNN is obtained more locally than LMPNN. Examination based on 10-fold cross-validation shows that the proposed multi-commission scheme can enhance the original (single-commission) LMPNN. Compared with two single-voter models: KNN and Bonferroni Mean Fuzzy k-Nearest Neighbors (BM-FKNN), the proposed MVMCNN also gives lower mean error rates as well as higher Precision, Recall, and F1 Score, indicating that the multi-voter model provides a better decision than the single-voter ones. |
| first_indexed | 2024-04-13T01:25:04Z |
| format | Article |
| id | doaj.art-146d6a0bc5cf4fe78ab25b508de2e577 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T01:25:04Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-146d6a0bc5cf4fe78ab25b508de2e5772022-12-22T03:08:38ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134862926302A multi-voter multi-commission nearest neighbor classifierSuyanto Suyanto0Prasti Eko Yunanto1Tenia Wahyuningrum2Siti Khomsah3School of Computing, Telkom University, Bandung, Indonesia; Corresponding author.School of Computing, Telkom University, Bandung, IndonesiaFaculty of Informatics, Institut Teknologi Telkom Purwokerto, IndonesiaFaculty of Informatics, Institut Teknologi Telkom Purwokerto, IndonesiaMany improved versions of k-nearest neighbor (KNN) have been proposed by minimizing total distances of multi k nearest neighbors (multi-voter) in each class instead of the majority voting, such as a local mean-based pseudo nearest neighbor (LMPNN) that give a better decision. In this paper, a new KNN variant called multi-voter multi-commission nearest neighbor (MVMCNN) is proposed to examine its benefits in enhancing the LMPNN. As the name suggests, MVMCNN uses some commissions: each calculates the total distance between the given query point (test pattern) and k pseudo nearest neighbors using the LMPNN scheme. The decision class is defined by minimizing those total distances. Hence, the decision in MVMCNN is obtained more locally than LMPNN. Examination based on 10-fold cross-validation shows that the proposed multi-commission scheme can enhance the original (single-commission) LMPNN. Compared with two single-voter models: KNN and Bonferroni Mean Fuzzy k-Nearest Neighbors (BM-FKNN), the proposed MVMCNN also gives lower mean error rates as well as higher Precision, Recall, and F1 Score, indicating that the multi-voter model provides a better decision than the single-voter ones.http://www.sciencedirect.com/science/article/pii/S1319157822000313Nearest neighbor classifierc-Means clusteringMachine learningMulti-commissionMulti-voter |
| spellingShingle | Suyanto Suyanto Prasti Eko Yunanto Tenia Wahyuningrum Siti Khomsah A multi-voter multi-commission nearest neighbor classifier Journal of King Saud University: Computer and Information Sciences Nearest neighbor classifier c-Means clustering Machine learning Multi-commission Multi-voter |
| title | A multi-voter multi-commission nearest neighbor classifier |
| title_full | A multi-voter multi-commission nearest neighbor classifier |
| title_fullStr | A multi-voter multi-commission nearest neighbor classifier |
| title_full_unstemmed | A multi-voter multi-commission nearest neighbor classifier |
| title_short | A multi-voter multi-commission nearest neighbor classifier |
| title_sort | multi voter multi commission nearest neighbor classifier |
| topic | Nearest neighbor classifier c-Means clustering Machine learning Multi-commission Multi-voter |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822000313 |
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