Saketha Nath, J and Bhattacharyya, C (2007) Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates. In: Proceedings of the SDM Conference.
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This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method.
|Item Type:||Conference Paper|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||17 Oct 2011 05:05|
|Last Modified:||17 Oct 2011 05:05|
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