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Abduction in Classification Tasks
[chapter]
2003
Lecture Notes in Computer Science
The aim of this paper is to show how abduction can be used in classification tasks when we deal with incomplete data. Some classifiers, even if based on decision tree induction like C4.5 [1], produce as output a set of rules in order to classify new given examples. Most of these rule-based classifiers make the assumption that at classification time we can know all about new given examples. Probabilistic approaches make rule-based classifiers able to get the most probable class, on the basis of
doi:10.1007/978-3-540-39853-0_18
fatcat:nhnka63amvh3xmy26ylgvazm6i