Evidence reasoning rule-based classifier with uncertainty quantification

Abstract

In Dempster–Shafer evidence theory (DST)-based classifier design, the newly proposed evidence reasoning (ER) rule can be used as a multi-attribute classifier to combine multiple pieces of evidence generated from quantitative samples or qualitative knowledge of many attributes. Different from the classical Dempster's combination (DC) rule and its improved forms, ER rule definitely distinguishes the reliability and importance weight of evidence. The former reflects the ability of a single attribute or the corresponding evidence to give correct classification results whereas the latter clarifies the relative importance of evidence when it is combined with other pieces of evidence. Here how to determine the reliability factor is a key problem because it is the connection between the preceding evidence acquisition and the following evidence combination with the importance weights. Therefore, the main aim of this paper is to present a universal method for obtaining the reliability factor by quantifying the uncertainties of samples and the generated evidence. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show the improved classifier can give higher classification accuracy than the original ER-based classifier without considering uncertainty quantification and other classical or mainstream classifiers.