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In this paper, we address the problem of making optimal product offers to customers of a retail bank by using techniques including Markov chains, genetic algorithms, mathematical programming, and design of experiments. Our challenges were large problem size, uncertainty about estimates of customer responses to product offers, and practical issues in training and implementation. The solution had an estimated financial impact of around $20 million; it also provided other intangible benefits, including structured decision making, the capability of performing what-if analysis, and portability to other markets and portfolios.

We consider the problem of detecting the presence of pneumoconiosis in a patient on the basis of evidence found in chest radiographs. Abnormalities pertaining to pneumoconiosis appear in the form of opacities of various sizes; the profusion of these opacities determines the stage of the disease. We present a multiresolution approach whereby we segment regions of interest (ROIs) from the X-Ray image at two levels -lung field and lung zone. We characterize each of these regions using a set of features and build support vector machine (SVM) classifiers that can predict whether or not the region contains any abnormalities. We combine these ROI-level predictions with a second stage SVM in order to get a prediction for the entire chest. Experimental validation shows that this approach provides good results.

Abstract. We consider the problem of learning reject option classifiers. The goodness of a reject option classifier is quantified using 0 − d − 1 loss function wherein a loss d ∈ (0, .5) is assigned for rejection. In this paper, we propose double ramp loss function which gives a continuous upper bound for (0 − d − 1) loss. Our approach is based on minimizing regularized risk under the double ramp loss using difference of convex (DC) programming. We show the effectiveness of our approach through experiments on synthetic and benchmark datasets. Our approach performs better than the state of the art reject option classification approaches.

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