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 …

Operations research and data mining already have a long-established common history.
Indeed, with the growing size of databases and the amount of data available, data mining …

In this contribution we identify the synergies of Operations Research and Data Mining.
Synergies can be achieved by integration of optimization techniques into Data Mining and …

The availability of today's online information systems rapidly increases the relevance of
dynamic decision making within a large number of operational contexts. Whenever a …

Abstract Support Vector Machines (SVMs), originally proposed for classifications of two
classes, have become a very popular technique in the machine learning field. For multi …

We propose a robust spherical separation technique aimed at separating two finite sets of
points and. Robustness concerns the possibility to admit uncertainties and perturbations in …

One recently proposed criterion to separate two data sets in discriminant analysis is to use a
hyperplane, which minimizes the sum of distances to it from all the misclassified data points …

Abstract The widely used Support Vector Machine (SVM) method has shown to yield good
results in Supervised Classification problems. Other methods such as Classification Trees …

In this paper, we investigate the performance of statistical, mathematical programming and
heuristic linear models for cost‐sensitive classification. In particular, we use five cost …

In ordinal regression, a score function and threshold values are sought to classify a set of
objects into a set of ranked classes. Classifying an individual in a class with higher …