Effective Method for Extracting Rules from Fuzzy Decision Trees based on Ambiguity and Classifiability

Crisp Decision trees (CDT) algorithms have been the most widely employed methodologies for symbolic knowledge acquisition. There are many methodologies have been presented to address the problems of the continuous data, multi-valued data, missing data, uncertainty data and noisy features. Recently, due to the widespread use of the fuzzy representation, a lot of researchers have utilized the fuzzy representation in decision trees to overcome the preceding problems. Fuzzy decision trees (FDT) are generalization for the CDT. FDTs are built by using fuzzy or crisp attributes and classes which often need pruning to reduce their size. FDTs have been successfully used to extract knowledge in uncertain classification problems. In this paper, we present a technique to build FDT by employing the ambiguity of attributes and classifiability of instance. Our technique builds a reduced FDT which does not need for applying the pruning algorithms to reduce the size. The paper also presents the results of a set of empirical studies conducted on a dataset of UCI Repository of Machine Learning Database that evaluate the effectiveness of our technique compared to Fussy Iterative Dichotomiser 3 (FID3), ambiguity, and FID3 with classifiability techniques. The studies show the effective of our technique in reducing the number of the extracted rules without loosing of the rules accuracy.