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Supervised classification and mathematical optimization

 

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Author: Carrizosa Priego, Emilio José
Romero Morales, María Dolores
Department: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Date: 2013-01
Published in: Computers & Operations Research, 40 (1), 150-165.
Document type: Article
Abstract: Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.
Cite: Carrizosa Priego, E.J. y Romero Morales, M.D. (2013). Supervised classification and mathematical optimization. Computers & Operations Research, 40 (1), 150-165.
Size: 372.7Kb
Format: PDF

URI: http://hdl.handle.net/11441/44821

DOI: 10.1016/j.cor.2012.05.015

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