Supervised Classification and Mathematical Optimization

Carrizosa, Emilio and Romero-Morales, Dolores (2013) Supervised Classification and Mathematical Optimization. Computers & Operations Research, 40 (1). pp. 150-165.

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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.

Item Type: Article
Keywords: Data Mining; Mathematical Optimization; Support Vector Machines; Interpretability; Cost Efficiency
Subject(s): Management science
Centre: Faculty of Management Science
Date Deposited: 01 Jun 2012 09:04
Last Modified: 23 Oct 2015 14:07
URI: http://eureka.sbs.ox.ac.uk/id/eprint/3817

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