" A Linear Combination of Classifiers via Rank Margin Maximization "


C. Marrocco, P. Simeone, F. Tortorella


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.

Doi :
Published in :

Download Publication

A file of this publication is available for download , for personal use only . Click on the download button and enter your email address in the box . You will receive an email with instructions to proceed to download