Decision template multi-label classification based on recursive dependent binary relevance

Abstract

In pattern recognition systems, ensemble techniques claim a potential performance improvement compared to single classifier approaches. Decision templates (DT) were proposed as a simple and effective method for combining continuous valued outputs of an ensemble of classifiers. In this paper, the concept of decision template single-label multi-class classifier combination is extended to the multi-label case. The different classifiers needed for a combination are obtained from the continuous re-estimation used in the Recursive Dependent Binary Relevance multi-label classifier. Each base classifier used in this work, delivers besides the class label, a continuous output for the class that can be used to assemble the DTs.