Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems’ complexity. However, after a series of interpolations, it is ...

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all ...

This paper presents an application of supervised machine learning approaches to the classification of the yeast S. cerevisiae gene expression data. Established feature selection techniques based on information gain ranking ...

This paper presents a data-driven approach for feature selection to address the common problem of dealing with high-dimensional data. This approach is able to handle the real-valued nature of the domain features, unlike ...

Probabilistic abduction extends conventional symbolic abductive reasoning with Bayesian inference methods. This allows for the uncertainty underlying implications to be expressed with probabilities as well as assumptions, ...

Despite increasing interest in the development of intelligent techniques to aid in the prevention and detection of crime, an important issue that has not yet been addressed by existing work is the use of knowledge based ...

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the ...

This paper presents an extension of the assumption-based truth maintenance system (ATMS), called “credibilistic ATMS,” which has the capability to cope with uncertain justifications and assumptions. Such justifications and ...

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques ...

Harmony search is a recently developed meta heuristic capable of solving discrete and continuous valued optimisation problems. However, the nature of pre-defined constant parameters limits the exploitation of the algorithm. ...