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Your search keyword '"Mencar, Corrado"' showing total 2 results
Start Over Author "Mencar, Corrado" Topic fuzzy algorithms
2 results on '"Mencar, Corrado"'

1. Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering.

Author

Casalino, Gabriella, Castellano, Giovanna, and Mencar, Corrado

Subjects
FUZZY algorithms, RIVERS, CLASSIFICATION algorithms, CLASSIFICATION, MACHINE learning
Abstract

A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based classification model. The proposed DISSFCM is capable of dynamically adapting the number of clusters to data streams, by splitting low-quality clusters so as to improve classification quality. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method in data stream classification. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Interface optimality in fuzzy inference systems

Author

Mencar, Corrado, Castellano, Giovanna, and Fanelli, Anna M.

Subjects
*SET theory, *MATHEMATICS, *FUZZY algorithms, *ALGORITHMS
Abstract

Abstract: In this paper we address the issue of designing optimal fuzzy interfaces, which are fundamental components of a fuzzy inference system. Due to the different roles of input and output interfaces, optimality conditions are analyzed separately for the two types of interface. We prove that input interfaces are optimal when based on a particular class of fuzzy sets called “bi-monotonic”, provided that mild conditions hold. The class of bi-monotonic fuzzy sets covers a broad range of fuzzy sets shapes, including convex fuzzy sets, so that the provided theoretical results can be applied to several fuzzy models. Such theoretical results are not applicable to output interfaces, for which a different optimality criterion is proposed. Such criterion leads to the definition of an optimality degree that measures the quality of a fuzzy output interface. Illustrative examples are presented to highlight the features of the proposed optimality degree in assessing the quality of output interfaces. [Copyright &y& Elsevier]

Published
2006
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