Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical meanings. The paper...

This paper proposes a fuzzy-rough method of maintaining CaseBased Reasoning (CBR) systems. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which take the form of fuzzy rules generated by the rough set technique. In paper [1], we have proposed a methodology for case base maintenance which ...

Extracting multilevel association rules in transaction databases is most commonly used tasks in data mining. This paper proposes a multilevel association rule mining using fuzzy concepts. This paper uses different fuzzy membership function to retrieve efficient association rules from multi level hierarchies that exist in a transaction dataset. In general, the data can spread into many hierarchi...

A major problem when conducting mining fuzzy association rules from the database (DB) is the large computation time and memory needed. In addition, the selection of fuzzy sets for each attribute of the database is very important because it will affect the quality of the mining rule. This paper proposes a method for mining fuzzy association rules using compressed database. We also use the approa...

The goal of this paper is to present a logic model with very good properties for the representation and evaluation of fuzzy association rules. For that we are going to combine two approaches. The first one is a model for the representation and evaluation of crisp association rules. The second one is a new proposal for the representation of imprecise properties (in particular for fuzzy sets) by ...

This paper shows how a small number of linguistically interpretable fuzzy rules can be extracted from numerical data for high-dimensional pattern classi®cation problems. One diculty in the handling of high-dimensional problems by fuzzy rule-based systems is the exponential increase in the number of fuzzy rules with the number of input variables. Another diculty is the deterioration in the com...

The aim of this paper is to propose a criterion to estimate the design, from experimental data, of a fuzzy inference system, when data are sparse. This lack of data is important and may improve the generalisation ability of fuzzy systems (0). Several methods have been proposed to obtain automatic fuzzy rules from sparse training data. In (0), the authors first construct fuzzy rules from collect...

In this paper, we introduce the design of fuzzy respective space-based neuro-fuzzy networks for pattern recognition. The proposed networks are realized by partitioning of the fuzzy respective input space to generate the fuzzy rules. The respectively partitioned spaces using fuzzy respective input space express the rules of the networks. The consequence part of the rules is represented by polyno...