Knowledge Discovery

This topic deals with the definition and realization of algorithms and techniques of Knowledge Discovery in the context of knowledge-based DSSs for i) tuning medical expert's knowledge, expressed in the form of linguistic variables, linguistic values and fuzzy rules, when it is only partially or qualitatively formalized; ii) extracting novel knowledge, expressed in the form of fuzzy rules, from data samples when no assessed knowledge is available or in order to complete existing one; iii) constructing membership functions for each linguistic variable involved in fuzzy rules starting from statistical data.

A revised scheme to compute horizontal covariances in an oceanographic 3D-VAR assimilation system

Farina, R., Dobricic, S., Storto, A., Masina, S., & Cuomo, S. (2015). A revised scheme to compute horizontal covariances in an oceanographic 3D-VAR assimilation system. Journal of Computational Physics284, 631-647.


Fuzzy Knowledge Construction from Statistical Data

The object of this research activity is to investigate methods and techniques to construct membership functions for each linguistic variable involved in the rules which a fuzzy DSS deals with, starting from statistical data. Our approach is meant to investigate the links potentially existing between fuzzy logic theory, possibility theory, probability theory and the observation of the frequency of events.

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Fuzzy Rule Extraction

The goal is to define innovative techniques and approaches for extracting novel knowledge, expressed in the form of fuzzy rules, from data samples when no assessed knowledge is available or in order to complete that existing one. Our approach relies on a six-steps data driven methodology to automatically build fuzzy DSSs, where each step can be approached using several strategies. The approach is based on the extraction of crisp rules from data, their fuzzification and the successive optimization with respect to the membership functions of the linguistic variables involved in the rules.

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Fuzzy Knowledge Adaptation

 The goal is to define advanced techniques and approaches for tuning medical expert's knowledge, expressed in the form of linguistic variables, linguistic values and fuzzy rules, mainly when it is just partially or qualitatively formalized. Our approach, devised to classification problems in medicine, is planned for optimizing the shapes of the membership functions for each linguistic variable involved in the rules by applying an adaptive technique based on an evolutionary algorithm, i.e. Differential Evolution.

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