Margin and domain integrated classification

Authors:
Yen-Lun Chen;Yuan F. Zheng
Affiliations:
Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio;Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Multi-category classification is an ongoing research topic with numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It handles multi-class problems as a combination of several target classes plus outliers. The basic idea behind the proposed approach is that target classes possess structured characteristics while outliers scatter around in the feature space. In our approach the domain description and large-margin discrimination are adjustable and therefore higher classification accuracy leads to better performance. The properties of MDIC are analyzed and the performance comparisons using synthetic and real data are presented.