A robust minimax approach to classification
The Journal of Machine Learning Research
Selected topics in robust convex optimization
Mathematical Programming: Series A and B
Convex Approximations of Chance Constrained Programs
SIAM Journal on Optimization
A modal symbolic classifier for interval data
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Efficient methods for robust classification under uncertainty in kernel matrices
The Journal of Machine Learning Research
Classifier fusion with interval-valued weights
Pattern Recognition Letters
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This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.