Gianluca Nastasi

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The paper presents an application of fuzzy logic to the problem of outliers detection. The overall purpose of the work is to point out anomalous data due different causes through a combination of several traditional methods for outliers detection in multivariate datasets and such combination is achieved through a fuzzy inference system. Moreover, the(More)
A common problem when dealing with preprocessing of real world data for a large variety of applications, such as classification and outliers detection, consists in fitting a probability distribution to a set of observations. Traditional approaches often require the resolution of complex equations systems or the use of specialized software for numerical(More)
Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to(More)
The paper analyses the issues behind strategies optimization of an existing automated warehouse for the steelmaking industry. Genetic Algorithms are employed to this purpose by deriving a custom chromosome structure as well as ad-hoc crossover and mutation operators. A comparison between three different solutions able to deal with multiobjective(More)
The paper presents a software which simulates an existing automated warehouse of steel tubes, including all the movements of the tubes packs conveyors as well as the management of all the operations for their storage and recovery for delivery to customers. The reason to develop such simulator lies in the needing to test new stocking strategies, that could(More)
An extremely important part of the finishing line is the pickling process, in which oxides formed during the hot rolling stage are removed from the surface of the steel sheets. The efficiency of the pickling process is mainly dependent on the nature of the oxide present at the surface of the steel, but, also, on process parameters such as bath composition(More)
In this paper a novel ensemble method (EM) for classification tasks is described. The proposed approach is based on the use of a set of classifiers, each of which is trained by exploiting a different subset of the available training data, which are created by partitioning the input space by means of a self organizing map (SOM) based clustering algorithm.(More)
The paper deals with the problem of the detection of rare patterns in an unbalanced dataset related to an industrial problem concerning the identification of manufactured defective metal products on the basis of product and process parameters. Within this work several approaches have been attempted for the development of a classifier whose performance are(More)