Efficient pairwise classification using local cross off strategy

Authors:
Mohammad Ali Bagheri;Qigang Gao;Sergio Escalera
Affiliations:
Faculty of Computer Science, Dalhousie University, Halifax, Canada;Faculty of Computer Science, Dalhousie University, Halifax, Canada;Computer Vision Center, Bellaterra, Spain,Dept. Matemtica Aplicada i Anlisi, Universitat de Barcelona, Barcelona, Spain
Venue:
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.