Class imbalance is a widespread problem in many classification tasks such as medical diagnosis and text categorization. To overcome this problem, we investigate one-class SVMs which can be trained to differentiate two classes on the basis of examples from a single class. We propose an improvement of one-class SVMs via a conformal kernel transformation as described in the context of binary SVM classifiers by. We tested this improved one-class SVM on a health care problem that involves discriminating 11% nosocomially infected patients from 89% non infected patients. The results obtained are encouraging: compared with three other SVM-based approaches to coping with class imbalance, one-class SVMs achieved the highest sensitivity recorded so far on the nosocomial infection dataset. However, the price to pay is a concomitant decrease specificity, and it is for domain experts to decide the proportion of false positive cases they are willing to accept in order to ensure treatment of all infected patients.
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