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.
The research has been partially supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
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Terlecki, P., Walczak, K. (2008). Adaptive Classification with Jumping Emerging Patterns. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_11
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DOI: https://doi.org/10.1007/978-3-540-79721-0_11
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