Abstract
In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classification. We provide an empirical evaluation of the decomposition method with use of various decomposition criteria.
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Latkowski, R. (2002). Incomplete Data Decomposition for Classification. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_54
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DOI: https://doi.org/10.1007/3-540-45813-1_54
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