Overly generalized predictions are a serious problem in concept classification. In particular, the boundaries among classes are not always clearly defined. For example, there are usually uncertainties in diagnoses based on data from biochemical laboratory examinations. Such uncertainties make the prediction be more difficult than noise-free data. To avoid such problems, the idea of fuzzy classification is proposed. This paper presents the basic definition of fuzzy classification trees along with their construction algorithm. Fuzzy classification trees is a new model that integrates the fuzzy classifiers with decision trees, that can work well in classifying the data with noise. Instead of determining a single class for any given instance, fuzzy classification predicts the degree of possibility for every class. Some empirical results the dataset from UCI Repository are given for comparing FCT and C4.5. Generally speaking, FCT can obtain better results than C4.5.
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