In 2011, Mallozzi et al. [Fuzzy Sets and Systems 165(2011) 98105] introduced a core-like concept (called F-core) and a balanced-like condition for games in which the worth of any coalition is given by means of a fuzzy interval. They proved that a balanced-like condition is necessary but not sufficient enough to guarantee the F-core to be non-empty. In this paper, we study deeply on this problem...

Fuzzy rule interpolation is an important technique for performing inferences with sparse rule bases. Even when given observations have no overlap with the antecedent values of any rule, fuzzy rule interpolation may still derive a conclusion. Nevertheless, fuzzy rule interpolation can only handle fuzziness but not roughness. Rough set theory is a useful tool to deal with incomplete knowledge, wh...

Because of the patient’s inconsistent data, uncertain Thyroid Disease dataset is appeared in the learning process: irrelevant, redundant, missing, and huge features. In this paper, Rough sets theory is used in data discretization for continuous attribute values, data reduction and rule induction. Also, Rough sets try to cluster the Thyroid relation attributes in the presence of missing attribut...

in this study, we introduce and study a concept of distributed fuzzymodeling. fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. in contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. i...

Rule-based classification systems have been widely used in real world applications because of the easy interpretability of rules. Many traditional rule-based classifiers prefer small rule sets to large rule sets, but small classifiers are sensitive to the missing values in unseen test data. In this paper, we present a larger classifier that is less sensitive to the missing values in unseen test...

The ‘hnique selling point” of fuzzy systems is usually the interpretability of its rule base. However, very often only the U C C U T U C ~ of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measurz the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of...

The development of efficient algorithms to correct faults in rule-based systems is very crucial in extending the verification and validation of rule sets and in the development of rule-based systems. While it is important to detect various kinds of faults in rule sets, it is also equally important to provide a user/expert with a set of heuristics that can aid in correcting these faults. In this...

Background: Prisoners, compared to the general population, are at greater risk of infection. Drug injection is the main route of HIV transmission, in particular in Iran. What would be of interest is to determine variables that govern drug injection among prisoners. However, one of the issues that challenge model building is incomplete national data sets. In this paper, we addressed the process ...

FuzzyBexa was the first algorithm to use a set covering approach for induction of fuzzy classification rules. It followed an iterated concept learning strategy, where rules are induced for each concept in turn. We present a new algorithm to allow also simultaneous concept learning and the induction of ordered fuzzy rule sets. When a proper rule evaluation function is used, simultaneous concept ...

Association rule mining is one of the widely using and simple concepts to find the frequent item sets from large number of datasets. While generating frequent item sets from a large dataset using association rule mining is not so efficient. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based evolutionary heuristic search methods used for ...