An approach to construct a new classifier called an intu-itionistic fuzzy decision tree is presented. Well known benchmark data is used to analyze the performance of the classifier. The results are compared to some other popular classification algorithms. Finally, the classifier behavior is verified while solving a real-world classification problem.
We present a novel approach to principal component analysis (PCA) for data expressed in terms of Atanassov's intuitionistic fuzzy sets (A-IFSs), i.e. using the degree of membership, non-membership and hesitation margin which was shown in our works to be a prerequisite for a meaningful analysis of A-IFS type data and information. This new approach to PCA for… (More)
In this paper we address the problem of how to measure information conveyed by an Atanassov's intuitionistic fuzzy set (A-IFS for short) and a related concept of knowledge that is explicitly context oriented and useful from the point of view of a specific purpose, notably related to decision making. We pay particular attention to the relationship between… (More)