Supervised Classification and Mathematical Optimization

Emilio Carrizosa, Dolores Romero Morales

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
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.
SprogEngelsk
TidsskriftComputers & Operations Research
Vol/bind40
Udgave nummer1
Sider150–165
ISSN0305-0548
DOI
StatusUdgivet - 2013
Udgivet eksterntJa

Emneord

  • Data mining
  • Mathematical optimization
  • Support vector machines
  • Interpretability
  • Cost efficiency

Citer dette

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Supervised Classification and Mathematical Optimization. / Carrizosa, Emilio; Morales, Dolores Romero.

I: Computers & Operations Research, Bind 40, Nr. 1, 2013, s. 150–165.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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