Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task
The random subspace method (RSM) has proved its excellence in numbers of pattern recognition tasks. However, the standard RSM is limited owing to the randomness in its feature selection procedure that is likely to lead to feature subset having poor class separability. In this paper, a proposal for a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-10-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2018.1512556 |
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| author | Hongyan Xu Tao Lin Yingtao Xie Zhi Chen |
| author_facet | Hongyan Xu Tao Lin Yingtao Xie Zhi Chen |
| author_sort | Hongyan Xu |
| collection | DOAJ |
| description | The random subspace method (RSM) has proved its excellence in numbers of pattern recognition tasks. However, the standard RSM is limited owing to the randomness in its feature selection procedure that is likely to lead to feature subset having poor class separability. In this paper, a proposal for a margin-based criterion has been presented for the evaluation of the true significance of the features, together with the true classification ability of base classifiers, so that both the training phase and integration phase of standard RSM could be enhanced. In the training phase, the random feature selection procedure is enhanced using a weighted random feature selection procedure, in order to improve the classification ability of the base classifier. In the integration phase, the simple majority voting strategy is enhanced using a weighted majority voting strategy for the purpose of assigning the base classifiers with poor classification ability to the lower voting weights. Experimental results on 30 benchmark datasets, together with 6 high-dimensional datasets prove that the recommended approach is capable of better providing classification ability to the usual classification task, in addition to the high-dimensional classification task. |
| format | Article |
| id | doaj-art-b2a67a9d1d2249c2920792a4167324d4 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2018-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-b2a67a9d1d2249c2920792a4167324d42025-08-20T02:00:16ZengTaylor & Francis GroupConnection Science0954-00911360-04942018-10-0130440942410.1080/09540091.2018.15125561512556Enriching the random subspace method with margin theory – a solution for the high-dimensional classification taskHongyan Xu0Tao Lin1Yingtao Xie2Zhi Chen3College of Computer Science, Sichuan UniversityCollege of Computer Science, Sichuan UniversityCollege of Computer Science, Sichuan UniversityCollege of Computer Science, Sichuan UniversityThe random subspace method (RSM) has proved its excellence in numbers of pattern recognition tasks. However, the standard RSM is limited owing to the randomness in its feature selection procedure that is likely to lead to feature subset having poor class separability. In this paper, a proposal for a margin-based criterion has been presented for the evaluation of the true significance of the features, together with the true classification ability of base classifiers, so that both the training phase and integration phase of standard RSM could be enhanced. In the training phase, the random feature selection procedure is enhanced using a weighted random feature selection procedure, in order to improve the classification ability of the base classifier. In the integration phase, the simple majority voting strategy is enhanced using a weighted majority voting strategy for the purpose of assigning the base classifiers with poor classification ability to the lower voting weights. Experimental results on 30 benchmark datasets, together with 6 high-dimensional datasets prove that the recommended approach is capable of better providing classification ability to the usual classification task, in addition to the high-dimensional classification task.http://dx.doi.org/10.1080/09540091.2018.1512556random subspace methodwrrsmmargin theoryweighted majority votinghigh-dimensional problem |
| spellingShingle | Hongyan Xu Tao Lin Yingtao Xie Zhi Chen Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task Connection Science random subspace method wrrsm margin theory weighted majority voting high-dimensional problem |
| title | Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task |
| title_full | Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task |
| title_fullStr | Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task |
| title_full_unstemmed | Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task |
| title_short | Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task |
| title_sort | enriching the random subspace method with margin theory a solution for the high dimensional classification task |
| topic | random subspace method wrrsm margin theory weighted majority voting high-dimensional problem |
| url | http://dx.doi.org/10.1080/09540091.2018.1512556 |
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