Nearest neighbour group-based classification
The purpose of group-based classification (GBC) is to determine the class label for a set of test samples, utilising the prior knowledge that the samples belong to same, but unknown class. This can be seen as a simplification of the well studied, but computationally complex, non-sequential compoun...
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| Format: | Article |
| Language: | en |
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2010
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| Online Access: | http://eprints.uthm.edu.my/7853/1/J5061_6e2744f4a2e5bf643b35fa74d280ad24.pdf http://eprints.uthm.edu.my/7853/ https://doi.org/10.1016/j.patcog.2010.05.010 |
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| _version_ | 1833418499085565952 |
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| author | Samsudin, Noor A. Bradley, Andrew P. |
| author_facet | Samsudin, Noor A. Bradley, Andrew P. |
| author_sort | Samsudin, Noor A. |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The purpose of group-based classification (GBC) is to determine the class label for a set of test samples,
utilising the prior knowledge that the samples belong to same, but unknown class. This can be seen as a
simplification of the well studied, but computationally complex, non-sequential compound classifica�tion problem. In this paper, we extend three variants of the nearest neighbour algorithm to develop a
number of non-parametric group-based classification techniques. The performances of the proposed
techniques are then evaluated on both synthetic and real-world data sets and their performance
compared with techniques that label test samples individually. The results show that, while no one
algorithm clearly outperforms all others on all data sets, the proposed group-based classification
techniques have the potential to outperform the individual-based techniques, especially as the (group)
size of the test set increases. In addition, it is shown that algorithms that pool information from the
whole test set perform better than two-stage approaches that undertake a vote based on the class labels
of individual test samples. |
| format | Article |
| id | my.uthm.eprints-7853 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2010 |
| record_format | eprints |
| spelling | my.uthm.eprints-78532022-10-17T06:16:34Z http://eprints.uthm.edu.my/7853/ Nearest neighbour group-based classification Samsudin, Noor A. Bradley, Andrew P. T Technology (General) The purpose of group-based classification (GBC) is to determine the class label for a set of test samples, utilising the prior knowledge that the samples belong to same, but unknown class. This can be seen as a simplification of the well studied, but computationally complex, non-sequential compound classifica�tion problem. In this paper, we extend three variants of the nearest neighbour algorithm to develop a number of non-parametric group-based classification techniques. The performances of the proposed techniques are then evaluated on both synthetic and real-world data sets and their performance compared with techniques that label test samples individually. The results show that, while no one algorithm clearly outperforms all others on all data sets, the proposed group-based classification techniques have the potential to outperform the individual-based techniques, especially as the (group) size of the test set increases. In addition, it is shown that algorithms that pool information from the whole test set perform better than two-stage approaches that undertake a vote based on the class labels of individual test samples. 2010 Article PeerReviewed text en http://eprints.uthm.edu.my/7853/1/J5061_6e2744f4a2e5bf643b35fa74d280ad24.pdf Samsudin, Noor A. and Bradley, Andrew P. (2010) Nearest neighbour group-based classification. Pattern Recognition, 43. pp. 3458-3467. https://doi.org/10.1016/j.patcog.2010.05.010 |
| spellingShingle | T Technology (General) Samsudin, Noor A. Bradley, Andrew P. Nearest neighbour group-based classification |
| title | Nearest neighbour group-based classification |
| title_full | Nearest neighbour group-based classification |
| title_fullStr | Nearest neighbour group-based classification |
| title_full_unstemmed | Nearest neighbour group-based classification |
| title_short | Nearest neighbour group-based classification |
| title_sort | nearest neighbour group-based classification |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7853/1/J5061_6e2744f4a2e5bf643b35fa74d280ad24.pdf http://eprints.uthm.edu.my/7853/ https://doi.org/10.1016/j.patcog.2010.05.010 |
| url_provider | http://eprints.uthm.edu.my/ |