Presentation 2003/10/17
An Efficient Method for Evolving Important Training Data
Takaharu KAWATSURE, Qiangfu ZHAO,
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Abstract(in English) Currently, we have proposed a method for generating comprehensible decision trees through evolution of training data. The biggest problem of this method is that computational effort is large. One of the reasons for this problem is that the number of generations for evolution is large. If we do not know when to stop evolution, the simplest way is to assume sufficient number of generations. In this paper, we try to solve the problem using validation set. The main idea is to validate the generated rule from an objective point of view and to stop the evolution at a proper point. Using this method, the number of generations can be greatly reduced. The error for training data might be increased, but the generalization ability will keep almost the same. The efficiency of the proposed method is verified through experiments with some public databases.
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Keyword(in English) Genetic algorithm / decision tree / cross validation / comprehensible learning
Paper # PRMU2003-129,NC2003-60
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Committee NC
Conference Date 2003/10/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Efficient Method for Evolving Important Training Data
Sub Title (in English)
Keyword(1) Genetic algorithm
Keyword(2) decision tree
Keyword(3) cross validation
Keyword(4) comprehensible learning
1st Author's Name Takaharu KAWATSURE
1st Author's Affiliation Multimedia Devices Lab, University of Aizu()
2nd Author's Name Qiangfu ZHAO
2nd Author's Affiliation Multimedia Devices Lab, University of Aizu
Date 2003/10/17
Paper # PRMU2003-129,NC2003-60
Volume (vol) vol.103
Number (no) 392
Page pp.pp.-
#Pages 5
Date of Issue