Learning and inference order in structured output elements classification

Authors:
Tomasz Kajdanowicz;Przemyslaw Kazienko
Affiliations:
Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland;Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2012

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Abstract

In the paper three learning and inference ordering approaches in the method for structured output classification are presented. As it was previously presented by authors, classification of single element in output structure can be performed by generalization of input attributes as well as already partially classified output elements [9]. The paper addresses crucial problem of how to order elements in the structured learning process to get greater final accuracy. The learning is performed by means of ensemble, boosting classification method adapted to structured prediction - AdaBoostSeq algorithm. Authors present several ordering heuristics for score function application in order to obtain better structured output classification accuracy.