Abstract
Decision-tree algorithms are one of the most popular applications in machine learning. The ID3 algorithm is an efficient method for building decision trees that form the basis for many decision tree programs. Fuzzy ID3 is an extension of the existing ID3 algorithm; it integrates fuzzy set theory and ID3 to overcome the effects of spurious precision in the data, to treat uncertainties in the data and to reduce the decision tree sensitivity to small changes in attribute values. In this paper, we introduce a modified version of fuzzy ID3 algorithm that integrates information gain and classification ambiguity to select the test attribute. The modified algorithm achieves better accuracy than the original Fuzzy ID3 as well as crisp programs such C4.5 on a wide range of datasets. We also introduce a new machine learning software tool based on fuzzy decision trees.
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