Mathematical model of bank scoring in conditions of insufficient data

Abstract

Recently, different methods of object classification using training datasets is actually. One of these methods is naive Bayesian classifier. Class of objects can consist of low number of elements. Such class is called poor class. In this paper we consider classification problem in poor class. Logical classifier doesn’t work in this case. Metric classifier can give good results if and only if there are quite dense set of metrically nearby classified objects in neighborhood of the considering object. Bayesian classifier reevaluates all hypotheses about belonging of the object to certain class. Therefore, Bayesian classifier can solve this classification problem. For example, we considered classic problem of bank scoring. This scoring is based on two criteria. Classified object has two belonging hypotheses. We can apply such reasoning for more difficult cases.