Supervised classification and mathematical optimization
Copyright policy )Supervised classification and mathematical optimization
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.
Ministerio de Ciencia e Innovación
Junta de Andalucía
- University of Seville Spain
- University of Oxford United Kingdom
Support vector machines, Classification and discrimination; cluster analysis (statistical aspects), cost efficiency, Mathematical optimization, Learning and adaptive systems in artificial intelligence, data mining, support vector machines, Cost efficiency, Applications of mathematical programming, mathematical optimization, Interpretability, interpretability, Data mining
Support vector machines, Classification and discrimination; cluster analysis (statistical aspects), cost efficiency, Mathematical optimization, Learning and adaptive systems in artificial intelligence, data mining, support vector machines, Cost efficiency, Applications of mathematical programming, mathematical optimization, Interpretability, interpretability, Data mining
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