In this paper we present an approach to neuro{fuzzy classiication that is able to learn fuzzy sets and fuzzy rules from data. The fuzzy rules that are created by this approach can be very well interpreted, however, they do not classify as good as the rules derived by sophisticated fuzzy clustering algorithms. They on the other hand supply usually unsatisfying fuzzy sets what makes it hard to in...

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First we briefly describe existing approaches to the handling ...

the paper analyses issues leading to errors in graphic object classifiers. thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andneuro-fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...

Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other one in the same record. To prevent generating this kind of misleading fuzz...

This paper proposes a rule selection method with the destructive learning algorithm to construct a compact fuzzy classification system with high performance. In this paper, first we construct a fuzzy classification system by generating fuzzy rules from numerical data, and consider the fuzzy classification system based on fuzzy rules a network. Then we select significant fuzzy rules from the rul...

This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp antecedents; in case of fuzzy booleans this set contains only two rules. It is shown that Zadeh's ext...

Knowledge systems are characterized by being active, uncertain and adaptive in pervasive computing environments, and Fuzzy Event-Condition-Action (ECA) rules can effectively describe these features of knowledge systems. To analyze the static structure and dynamic behavior of fuzzy ECA rules, a fuzzy colored time Petri net model (FCTPN) is proposed. The FCTPN is able to model the fuzzy ECA rules...

In essence, data mining consists of extracting knowledge from data. This paper proposes an evolutionary system for discovering fuzzy classi cation rules. Fuzzy logic is useful for data mining especially in the case for performing classi cation task. Three methods were used to extract fuzzy classi cation rules using Evolutionary Algorithms: (1) genetic selection small number of large number of f...

fuzzy rules based classifier systems (FRBS) have gained more popularity in recent years. This classifier suffers from the problem of pattern space partitioning, to reach a compact set of rules that provides high classification power. In this paper, we propose an adaptive hierarchical fuzzy partitioning method based on tree decomposition. This decomposition is controlled by the grade of certaint...