Characterization of Relevance and Irrelevance in Empirical Learning Methods Based on Rough Sets and Matroid Theory

Shusaku Tsumoto, Hiroshi Tanaka

One of the most important characteristics of empirical learning methods, such as AQ, ID3(CART), C4.5 and CN2, is that they find variables which are relevant to classification. In this paper, we define relevance in empirical classifier as relevance of each given attribute to apparent or predictive classification, and describe this type of relevance in terms of rough sets and matroid theory. The results show that these algorithms can be viewed as the greedy algorithms searching for apparent classification and that their weight functions may play an important role in predictive classification.


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