Supervised multi-class classification with adaptive and automatic parameter tuning

Authors:
Chao Chen;Mei-Ling Shyu;Shu-Ching Chen
Affiliations:
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL;Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL;School of Computing and Information Sciences, Florida International University, Miami, FL
Venue:
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Year:
2009

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Abstract

In this paper, a classification framework is developed to address the issue that empirical determination of the parameters and their values typically makes a classification framework less adaptive and general to different data sets and application domains. Experimental results show that our proposed framework achieves (1) better performance over other comparative supervised classification methods, (2) more robust to imbalanced data sets, and (3) smaller performance variance to different data sets.