Fabio Massimo Frattale Mascioli

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A high automation degree is one of the most important features of data driven modeling tools and it should be taken into consideration in classification systems design. In this regard, constructive training algorithms are essential to improve the automation degree of a modeling system. Among neuro-fuzzy classifiers, Simpson's (1992) min-max networks have(More)
This paper presents a constructive training algorithm for supervised neural networks. The algorithm relies on a topological approach, based on the representation of the mapping of interest onto the binary hypercube of the input space. It dynamically constructs a two-layer neural network by involving successively binary examples. A convenient treatment of(More)
This paper presents the SPARE C++ library, an open source software tool conceived to build generic pattern recognition and soft computing systems. The library follows the requirement of the generality: most of the implemented algorithms are able to process user-defined input data types transparently, such as labeled graphs and generalized sequences, as well(More)
In this paper we propose a classifier for generalized sequences that is conceived in the granular computing framework. The classification system processes the input sequences of objects by means of a suited interplay among dissimilarity and clustering based techniques. The core data mining engine retrieves information granules that are used to represent the(More)
Due to the intrinsic complexity of real-world power distribution lines, which are highly non-linear and time-varying systems, modeling and predicting a general fault instance is a very challenging task. Power outages can be experienced as a consequence of a multitude of causes, such as damage of some physical components or grid overloads. Smart grids are(More)
In this paper we present an innovative procedure for sequence mining and representation. It can be used as its own in Data Mining problems or as the core of a classification system based on a Granular Computing approach to represent sequences in a suited embedding space. By adopting an inexact sequence matching procedure, the algorithm is able to extract a(More)
Sensorial signals are processed by brain by relying on their signi"cant aspects. Fuzzy and scale-based approaches try to imitate this mechanism. In the paper, a new clustering algorithm is proposed which makes use of both approaches. It is characterised by a hierarchical splitting process guided by the scale-based approach and based on the repetitive(More)