Modified blame-based noise reduction for concept drift

Authors:
Ning Lu;Guangquan Zhang;Jie Lu
Affiliations:
Decision Systems & e-Service Intelligence, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, A ...;Decision Systems & e-Service Intelligence, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, A ...;Decision Systems & e-Service Intelligence, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, A ...
Venue:
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2012

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Abstract

Competence enhancement plays an important role in case-base editing. Traditional competence enhancement methods tend to omit the evolving nature of a case-based learner, but take the whole case-base as a static training set. This may seriously delay or even prohibit a learner from learning new concepts, when concept drifts. This paper proposes a Modified Blame Based Noise Removal algorithm (M-BBNR). Our MBBNR algorithm preserves some potential noise cases, in case of representing novel concepts. Experiment show that with such a "wait-and-see" policy, the developed M-BBNR algorithm outperforms other famous competence enhancement methods on real world dataset and is able to tuning the case-base according to the concept drift effectively.