Artículo
Supervised classification and mathematical optimization
Autor/es | Carrizosa Priego, Emilio José
Romero Morales, María Dolores |
Departamento | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2013-01 |
Fecha de depósito | 2016-09-08 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | MTM2009-14039
FQM-329 |
Cita | Carrizosa Priego, E.J. y Romero Morales, M.D. (2013). Supervised classification and mathematical optimization. Computers & Operations Research, 40 (1), 150-165. |
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