Adaptive classification with jumping emerging patterns

Authors:
Pawel Terlecki;Krzysztof Walczak
Affiliations:
Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland;Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland
Venue:
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Year:
2008

Citing 6
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Abstract

In this paper a generic adaptive classification scheme based on a classifier with reject option is proposed. A testing set is considered iteratively, accepted, semi-labeled cases are used to modify the underlying hypothesis and improve its accuracy for rejected ones. We apply our approach to classification with jumping emerging patterns (JEPs). Two adaptive versions of JEP-Classifier, by support adjustment and by border recomputation, are discussed. An adaptation condition is formulated after distance and ambiguity rejection strategies for probabilistic classifiers. The behavior of the method is tested against real-life datasets.