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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Published in | Journal of Information and Communication Convergence Engineering, 11(4) Vol. 11; no. 4; pp. 258 - 267 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
한국정보통신학회
31.12.2013
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Subjects | |
Online Access | Get full text |
ISSN | 2234-8255 2234-8883 2234-8883 |
DOI | 10.6109/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 |
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Bibliography: | G704-SER000003196.2013.11.4.005 |
ISSN: | 2234-8255 2234-8883 2234-8883 |
DOI: | 10.6109/jicce.2013.11.4.258 |