Presentation | 2006-12-05 Fast Induction of NNC-Trees Based on Dimensionality Reduction Naoki TOMINAGA, Qiangfu ZHAO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | An NNC-Tree is a decision tree whose non-terminal node contains a nearest neighbor classifier. Compared with the conventional decision trees, the decision rules corresponding to NNC-Trees are more compact, more accurate, and more comprehensible. The induction of NNC-Trees, however, is relatively time-consuming. Thus, to speedup the induction process is an important issue to make NNC-Trees practically useful. To speedup the induction process of NNC-Trees, this paper proposes to reduce the dimensionality of the input space using principal components analysis before induction. Experiments on several public databases show that the proposed method is more effective than the existing algorithm when the number of features is large. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | NNC-Tree / nearest neighbor classifier / principal components analysis / dimensionality reduction / pattern recognition |
Paper # | NC2006-81 |
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Committee | NC |
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Conference Date | 2006/11/28(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Fast Induction of NNC-Trees Based on Dimensionality Reduction |
Sub Title (in English) | |
Keyword(1) | NNC-Tree |
Keyword(2) | nearest neighbor classifier |
Keyword(3) | principal components analysis |
Keyword(4) | dimensionality reduction |
Keyword(5) | pattern recognition |
1st Author's Name | Naoki TOMINAGA |
1st Author's Affiliation | School of Computer Science and Engineering, The University of Aizu Tsuruga() |
2nd Author's Name | Qiangfu ZHAO |
2nd Author's Affiliation | School of Computer Science and Engineering, The University of Aizu Tsuruga |
Date | 2006-12-05 |
Paper # | NC2006-81 |
Volume (vol) | vol.106 |
Number (no) | 407 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |