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Cost Sensitive SVM  
A new procedure for learning costsensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the costsensitive SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to costsensitive classification, in a manner that guarantees consistency with the costsensitive Bayes risk, and associated Bayes decision rule. This ensures that optimal decision rules, under the new hinge loss, implement the Bayesoptimal costsensitive classification boundary. Minimization of the new hinge loss is shown to be a generalization of the classic SVMoptimization problem, and can be solved by identical procedures. The resulting algorithm avoids the shortcomings of previous approaches to costsensitive SVM design, and has superior experimental performance.  
Experimental Results: 


Publications: 
Risk minimization, probability elicitation, and costsensitive SVMs Hamed MasnadiShirazi and Nuno Vasconcelos. International Conference on Machine Learning (ICML), 2010. (acceptance rate 20%) [pdf]
 
Contact:  Nuno Vasconcelos, Hamed MasnadiShirazi 
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