Fuzzy classification trees for data analysis

Overly generalized predictions are a serious problem in concept classi!cation. In particular, the boundaries among classes are not always clearly de!ned. For example, there are usually uncertainties in diagnoses based on data from biochemical laboratory examinations. Such uncertainties make the prediction be more di4cult than noise-free data. To avoid such problems, the idea of fuzzy classi$cation is proposed. This paper presents the basic de!nition of fuzzy classi!cation trees along with their construction algorithm. Fuzzy classi$cation trees is a new model that integrates the fuzzy classi!ers with decision trees, that can work well in classifying the data with noise. Instead of determining a single class for any given instance, fuzzy classi!cation 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. c © 2002 Elsevier Science B.V. All rights reserved.