| Presentation | 2003/9/8 Feature subset selection using restriction kernels Ken SADOHARA, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper presents a new feature subset selection algorithm than can take into account higher order correlation between variables. The algorithm is a kind of wrapper methods using Support Vector Machines (SVMs) for learning classifiers represented as hyperplanes spanned by combinations of variables. It is known that kernel functions enable efficient learning of the high dimensional hyperplanes, while this paper considers another use of kernel functions for analyzing the learned classifiers to determine irrelevant variables. In the analysis, the algorithm computes the restriction of a classifier obtained by removing the components containing a variable, and the variable is identified as irrelevant if the restriction discriminates data as well as the classifier. Although there exist numerous components to be removed, it is shown that the restriction can be computed efficiently by using restriction kernels. It is also shown that the presented algorithm outperforms existing algorithms in empirical studies on the synthetic data sets. Furthermore, the algorithm is applied to text categorization tasks and an encouraging result is obtained. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | feature selection / support vector machine / kernel methods / text categorization |
| Paper # | AI2003-46 |
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| Conference Information | |
| Committee | AI |
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| Conference Date | 2003/9/8(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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| Paper Information | |
| Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Feature subset selection using restriction kernels |
| Sub Title (in English) | |
| Keyword(1) | feature selection |
| Keyword(2) | support vector machine |
| Keyword(3) | kernel methods |
| Keyword(4) | text categorization |
| 1st Author's Name | Ken SADOHARA |
| 1st Author's Affiliation | National Institute of Advanced Industrial Science and Technology (AIST)() |
| Date | 2003/9/8 |
| Paper # | AI2003-46 |
| Volume (vol) | vol.103 |
| Number (no) | 305 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |