One of the main obstacles facing current intelligent pattern recognition applications is that of dataset dimensionality. To enable these systems to be effective, a redundancy-removing step is usually carried out beforehand. ...

Within the past decades, many fuzzy aggregationtechniques, ordered weighted averaging (OWA) in particular, have proven effective for a wide range of information processing tasks, such as decision making, image analysis, ...

For some time, learning Bayesian networks has been both feasible and useful in many problems domains. Recently research has been done on learning equivalence classes of Bayesian networks, i.e. structures that capture all ...

Reasoning with fuzzy rule-based models has been widely applied to perform various real world classification tasks. The main advantage of this approach is that it supports inferences in the way people think and make judgements. ...

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 ...