Construction of sequential classifier based on broken stick model

Abstract

This paper presents the problem of building the sequential model of the classification task. In our approach the structure of the model is built in the learning phase of classification. In this paper a split criterion based on the broken stick model is proposed. The broken stick distribution is created for each column of the confusion matrix. The split criterion is associated with the analysis of the received distributions. The obtained results were verified on ten data sets. Nine data sets come from UCI repository and one is a real-life data set. © 2013 Springer-Verlag.