Similarity-driven Defuzzification of Fuzzy Tuples for Entropy-based Data Classification Purposes

In this paper, we introduce a new method which lets us utilize uncertain data for precise decision rules learning. We focus our investigation on a proximity-based fuzzy relational database as it provides convenient mechanisms for the storage and interpretation of uncertain information. In proximity-based fuzzy databases the lack of certainty about obtained information can be represented via insertion of multiple (i.e. non-atomic) attribute values. In addition the database extends classical equivalence relations with fuzzy proximity relations, which provide users with extraordinary analytical capabilities. In this paper we take advantage of both of these properties when developing our approach to induction of decision trees from imperfect information.