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- W2006341570 abstract "Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method." @default.
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- W2006341570 date "2003-06-01" @default.
- W2006341570 modified "2023-09-26" @default.
- W2006341570 title "Fuzzy Classification Rule Learning by Decision Tree Induction" @default.
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- W2006341570 doi "https://doi.org/10.5391/ijfis.2003.3.1.044" @default.
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