JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
A New Incremental Learning Algorithm with Probabilistic Weights Using Extended Data Expression |
| 영문제목(English Title) |
A New Incremental Learning Algorithm with Probabilistic Weights Using Extended Data Expression |
| 저자(Author) |
Kwangmo Yang
Anastasiya Kolesnikova
Won Don Lee
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| 원문수록처(Citation) |
VOL 11 NO. 04 PP. 0258 ~ 0267 (2013. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn , an incremental ensemble-based algorithm
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| 키워드(Keyword) |
C4.5
Ensemble-based algorithm
Incremental learning
Learn++
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| 파일첨부 |
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