Presentation | 2004/11/12 Induction of Decision Trees from the Distance Space Generated by NNC Takaharu Kawatsure, Qiangfu Zhao, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Nearest Neighbor Classifier (NNC) is one of the simplest methods for pattern recognition. Currently, we have proposed an R^4-rule that can make the smallest or nearly smallest NNCs. On the other hand, decision tree (DT) is often considered as one of the most comprehensible methods. If the data set is large, however, DTs may become too large and no longer comprehensible. In this paper, we propose a new method for inducing DTs from a distance space generated by an NNC. The NNC itself is designed by the R^4-rule. Using this method, we can induce more compact and more comprehensible DTs. The efficiency of the method is verified through experiments with several public databases. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Pattern recognition / decision tree / nearest neighbor classifier / R^4-rule / distance space |
Paper # | PRMU2004-114,HIP2004-54 |
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Committee | HIP |
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Conference Date | 2004/11/12(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Human Information Processing (HIP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Induction of Decision Trees from the Distance Space Generated by NNC |
Sub Title (in English) | |
Keyword(1) | Pattern recognition |
Keyword(2) | decision tree |
Keyword(3) | nearest neighbor classifier |
Keyword(4) | R^4-rule |
Keyword(5) | distance space |
1st Author's Name | Takaharu Kawatsure |
1st Author's Affiliation | The University of Aizu() |
2nd Author's Name | Qiangfu Zhao |
2nd Author's Affiliation | The University of Aizu |
Date | 2004/11/12 |
Paper # | PRMU2004-114,HIP2004-54 |
Volume (vol) | vol.104 |
Number (no) | 450 |
Page | pp.pp.- |
#Pages | 6 |
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