This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feature selection, and unsupervised learning. The ...

One of the main obstacles facing the application of computational intelligence technologies in pattern recognition (and indeed in many other tasks) is that of dataset dimensionality. To enable pattern classifiers to be ...

Fuzzy rule interpolation (FRI) is well known for reducing the complexity of fuzzy models and making inference possible in sparse rule-based systems. However, in practical fuzzy applications with inter-connected rule bases, ...

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 ...

Many search strategies have been exploited in implementing feature selection, in an effort to identify smaller and better subsets. Such work typically involves the use of heuristics in one form or another. In this paper ...

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of ...