Learn More
—In this paper, we propose the use of a multiobjective evolutionary approach to generate a set of linguistic fuzzy-rule-based systems with different tradeoffs between accuracy and in-terpretability in regression problems. Accuracy and interpretabil-ity are measured in terms of approximation error and rule base (RB) complexity, respectively. The proposed(More)
The automated extraction of the pulmonary parenchyma in CT images is the most crucial step in a computer-aided diagnosis (CAD) system. Actually, the following step of analysis of the lung's internal structure, aimed at lesion detection and diagnosis, works on the identified pulmonary regions.In this paper we describe a method, consisting of an appropriate(More)
One of the challenges in Risk Analysis and Management (RAM) is identifying the relationships between risk factors and risks. In this paper we propose using Extended Fuzzy Cognitive Maps to analyze the relationships between risk factors and risks. E-FCMs are suggested by Hagiwara to represent causal relationships more naturally. The main differences between(More)
We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms of sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents of the classifier rules). Then, we use(More)