Different studies have proposedmethods formining fuzzy association rules fromquantitative data, where themembership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extr...

Fuzzy rule-based systems have been mainly used as a convenient tool for synthesizing control laws from data. Recently, in a knowledge representation-oriented perspective, a typology of fuzzy rules has been laid bare, by emphasizing the distinction between implicative and conjunctive fuzzy rules. The former describe pieces of generic knowledge either tainted with uncertainty or tolerant to simil...

A description of quantitative dependencies by a novel type of fuzzy rules like ‘If X is SMALL then Y is QUICKLY INCREASING’ is considered. The use of such rules for representation of perception based and numerical information about dependencies between variables is discussed. These rules are based on a granulation of directions of function change or slope values. Perception based information gi...

Fuzzy Rules have been shown to be very useful in modeling relationships between variables that have a high degree of uncertainty or ambiguity. A major question in regards to learning fuzzy rule bases is how to handle interactions between rules of overlapping coverage. Structures, such as Yager’s HPS (Hierarchical Prioritized Structure), have been proposed to answer this question. In this paper,...

In [13], we proposed that there are three kinds of negative relations which contradictory negative relation, opposite negative relation and medium negative relation in fuzzy information, and that defined a new fuzzy set FSCOM with contradictory negation, opposite negation and medium negation. In order to show applicability of FSCOM, this paper apply FSCOM to fuzzy decision making in an actual e...

This paper is focused on the evolution of the theory of fuzzy IF-THEN rules and its contribution to the establishment of fuzzy logic. I advocate that Hájek’s fuzzy logic is a right methodology for development of special logical theories. A theory of fuzzy IF-THEN rules (as a special theory in this sense) is proposed. This theory is for all practitioners who want to create a system of fuzzy IF-T...

Fuzzy rules are usually generated by experts in the area, especially for control problems with only a few inputs. With an increasing number of variables, the number of rules is increasing exponentially, which makes more difficult for experts to define the rule set for good system performance. In solving this, researchers have looked into hybrid the fuzzy based to enhance or reduce complexity. W...

Fuzzy rule-based edge detection using multiscale edge images is proposed. In this method, the edge image is obtained by fuzzy approximate reasoning from multiscale edge images which are obtained by derivative operators with various window sizes. The effect of utilizing multiscale edge images for edge detection is already known, but how to design the rules for deciding edges from multiscale edge...

Classification Rules (CRs) are often discovered in the form of ‘If-Then’ Production Rules (PRs). PRs, being high level symbolic rules, are comprehensible and easy to implement. However, they are not capable of dealing with cognitive uncertainties like vagueness and ambiguity imperative to real word decision making situations. Fuzzy Classification Rules (FCRs) based on fuzzy logic provide a fram...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...