Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10347487 | Computers & Operations Research | 2013 | 16 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Emilio Carrizosa, Dolores Romero Morales,