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- W2029492325 abstract "In this paper, we give a fuzzy decision tree (simply FDT) induction algorithm, named FDT Ambig , to handle the classification with discrete attributes through the uncertainty reduction. In FDT Ambig , the uncertainty is measured with classification ambiguity. FDT Ambig selects the attribute which will cause the further reduction of uncertainty as the expanded attribute for each decision node. The experimental result shows that FDT Ambig has the better generalization capability in comparison with the FDT induced with classification entropy (FDT Entr )." @default.
- W2029492325 created "2016-06-24" @default.
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- W2029492325 date "2015-01-01" @default.
- W2029492325 modified "2023-10-02" @default.
- W2029492325 title "Inducting Fuzzy Decision Tree Based on Discrete Attributes through Uncertainty Reduction" @default.
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- W2029492325 doi "https://doi.org/10.4028/www.scientific.net/amm.719-720.1223" @default.
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