Linguistic Decision Tree Induction
نویسندگان
چکیده
Label Semantics is a random set based framework for modeling imprecise concepts where the degree of appropriateness of a linguistic expression as a description of a certain value is measured in terms of how the set of appropriate labels for that value varies across a population. An approach to decision tree induction based on this framework was studied. A new decision tree learning algorithm was proposed and its performance applied in real-world data sets was compared with the C4.5 algorithm.
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تاریخ انتشار 2003