A linear combination of classifiers via rank margin maximization

The method we present aims at building a weighted linear combination of already trained dichotomizers, where the weights are determined to maximize the minimum rank margin of the resulting ranking system. This is particularly suited for real applications where it is difficult to exactly determine key parameters such as costs and priors. In such cases ranking is needed rather than classification. A ranker can be seen as a more basic system than a classifier since it ranks the samples according to the value assigned by the classifier to each of them. Experiments on popular benchmarks along with a comparison with other typical rankers are proposed to show how effective can be the approach.