Browsing by Author "Conforti, Domenico"
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Item Applicazione della programmazione stocastica nella gestione delle sale operatorie(2011) Pirelli, Domenico; Conforti, Domenico; Bruni, Maria Elena; Grandinetti, LucioItem Innovativi multiclassificatori con applicazione ai sistemi di supporto alle decisioni cliniche(2017-07-03) Groccia, Maria Carmela; Conforti, Domenico; Canonaco, MarcelloItem MKL - CT: Multiple kernel learning for censored targets(2014-06-06) Lagani, Vincenzo; Conforti, Domenico; Grandinetti, LucioItem Modelli integrati di analisi di sopravvivenza applicati alla prognosi del trapianto renale(2013-12-23) Lofaro, Danilo; Conforti, Domenico; Guido, RositaItem Un framework di soluzione ad alto livello per problemi di classificazione basato su approcci metaeuristici(2014-05-27) Candelieri, Antonio; Grandinetti, Lucio; Conforti, DomenicoThis work deals with the development and implementation of a high-level classification framework which combines parameters optimization of a single classifier with classifiers ensemble optimization, through meta-heuristics. Support Vector Machines (SVM) is used for learning while the meta-heuristics adopted and compared are Genetic-Algorithms (GA), Tabu-Search (TS) and Ant Colony Optimization (ACO). Single SVM optimization usually concerns two approaches: searching for optimal parameter values of a SVM with a fixed kernel (Model Selection) or with a linear combination of basic kernels (Multiple Kernel Learning), both approaches have been taken into account. Adopting meta-heuristics avoids to perform time consuming grid-approach for testing several classifier configurations. In particular, starting from canonical formulation of GA, this study proposes some changes in order to take into account specificities of classification learning. Proposed solution has been extensively tested on 8 classification datasets (5 of them are of public domain) providing reliable solutions and showing to be effective. In details, unifying Model Selection, Multiple Kernel Learning and Ensemble Learning on a single framework proved to be a comprehensive and reliable approach, and showing that best solutions have been identified by one of the strategies depending on decision problem and/or available data. Under this respect, the proposed framework may represent a new effective and efficient high-level SVM classification learning strategy.