### Resumé

Sprog | Engelsk |
---|---|

Tidsskrift | Computers & Operations Research |

Vol/bind | 40 |

Udgave nummer | 1 |

Sider | 150–165 |

ISSN | 0305-0548 |

DOI | |

Status | Udgivet - 2013 |

Udgivet eksternt | Ja |

### Emneord

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

### Citer dette

*Computers & Operations Research*,

*40*(1), 150–165. DOI: 10.1016/j.cor.2012.05.015

}

*Computers & Operations Research*, bind 40, nr. 1, s. 150–165. DOI: 10.1016/j.cor.2012.05.015

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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Supervised Classification and Mathematical Optimization

AU - Carrizosa,Emilio

AU - Morales,Dolores Romero

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

T2 - Computers & Operations Research

JF - Computers & Operations Research

SN - 0305-0548

IS - 1

ER -