Supervised Classification and Mathematical Optimization

Emilio Carrizosa, Dolores Romero Morales

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
JournalComputers & Operations Research
Volume40
Issue number1
Pages (from-to)150–165
ISSN0305-0548
DOIs
Publication statusPublished - 2013
Externally publishedYes

Cite this

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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.",
keywords = "Data mining, Mathematical optimization, Support vector machines, Interpretability , Cost efficiency",
author = "Emilio Carrizosa and {Romero Morales}, Dolores",
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Supervised Classification and Mathematical Optimization. / Carrizosa, Emilio; Romero Morales, Dolores .

In: Computers & Operations Research, Vol. 40, No. 1, 2013, p. 150–165.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Supervised Classification and Mathematical Optimization

AU - Carrizosa, Emilio

AU - Romero Morales, Dolores

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Data mining

KW - Mathematical optimization

KW - Support vector machines

KW - Interpretability

KW - Cost efficiency

U2 - 10.1016/j.cor.2012.05.015

DO - 10.1016/j.cor.2012.05.015

M3 - Journal article

VL - 40

SP - 150

EP - 165

JO - Computers & Operations Research

JF - Computers & Operations Research

SN - 0305-0548

IS - 1

ER -