The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the...

We introduce the idea of Characteristic Regions to solve a classification problem. By identifying regions in which classes are dense (i.e. many observations) and also relevant (for discrimination) we can characterize the different classes. These Characteristic Regions are used to generate a...

In order to group the observations of a data set into a given number of clusters, an ‘optimal’ subset out of a greater number of explanatory variables is to be selected. The problem is approached by maximizing a quality measure under certain restrictions that are supposed to keep the subset...

Sometimes one may be confronted with classification problems where classes are constituted of several subclasses that possess different distributions and therefore destroy accurate models of the entire classes as one similar group. An issue is modelling via local models of several subclasses. In...

One serious problem in deep-hole drilling is the formation of a dynamic disturbance called spiralling which causes holes with several lobes. Since such lobes are a severe impairment of the bore hole quality the formation of spiralling has to be prevented. Gessesse et al. [2] explain spiralling...

In classification, with an increasing number of variables, the required number of observations grows drastically. In this paper we present an approach to put into effect the maximal possible variable selection, by splitting a K class classification problem into pairwise problems. The principle...