Abstract
In this paper, we give a fuzzy decision tree (simply FDT) induction algorithm, named FDTAmbig, to handle the classification with discrete attributes through the uncertainty reduction. In FDTAmbig, the uncertainty is measured with classification ambiguity. FDTAmbig selects the attribute which will cause the further reduction of uncertainty as the expanded attribute for each decision node. The experimental result shows that FDTAmbig has the better generalization capability in comparison with the FDT induced with classification entropy (FDTEntr).
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