Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be e...

Decision tree construction is an important data-mining problem. In this paper we introduce an approach of using cumulative information estimations for fuzzy decision tree induction. We present new type of fuzzy decision tree: ordered tree. This tree is oriented to parallel processing of attributes with differing costs.

Fuzzy Decision Tree induction is a powerful methodology to extract human interpretable classification rules. The induction of fuzzy decision tree is done using popular Fuzzy ID3 algorithm [1, 2]. The other parameters which influence the performance of fuzzy decision tree are cut standard parameter  and leaf selection threshold parameter th  . Generally these parameters are to be selected heur...

Fuzzy Decision Trees (FDT’s) are one of the most popular choices for learning and reasoning from dataset. They have undergone a number of alterations to language and measurement uncertainties. However, they are poor in classification accuracy. In this paper, Neuro -fuzzy decision tree ( a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT’s classification...

Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree. It proposes a fuzzy decision tree induction method in iris flower data set, obtaining the entropy from the distance bet...

As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes...

Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree. It proposes a fuzzy decision tree induction method in iris flower data set, obtaining the entropy from the distance bet...

Customer Retention is a major aspect of the growth of any business. It is always good to retain those customers who are more loyal or can be converted to be loyal. In this paper, a fuzzy decision tree based approach is proposed in combination with Z-shaped curved membership function to find the customer loyalty and has been compared with the approach of fuzzy decision tree with Gaussian members...

In operations research one often faces scenarios and phenomena exhibiting imprecision and uncertainty. Stochastic aspects of these scenarios are often accounted for by means of probabilistic models. In addition to these models, fuzzy augmentation to traditional decision analysis has been advocated as a possible method that takes into account imprecision in quantities available to the decision m...