A New Incremental Learning Algorithm with Probabilistic Weights Using Extended Data Expression

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 imp...

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Bibliographic Details
Published inJournal of Information and Communication Convergence Engineering, 11(4) Vol. 11; no. 4; pp. 258 - 267
Main Authors Yang, Kwangmo, Kolesnikova, Anastasiya, Lee, Won Don
Format Journal Article
LanguageEnglish
Published 한국정보통신학회 31.12.2013
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ISSN2234-8255
2234-8883
2234-8883
DOI10.6109/jicce.2013.11.4.258

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Summary: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. KCI Citation Count: 1
Bibliography:G704-SER000003196.2013.11.4.005
ISSN:2234-8255
2234-8883
2234-8883
DOI:10.6109/jicce.2013.11.4.258