Learning from data with uncertain labels by boosting credal classifiers
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
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A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster–Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.