Intelligent data analysis with fuzzy decision trees

Intelligent data analysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the data analysis process, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic data analysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent data analysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.