Supervised Ordering — An Empirical Survey
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Statistical models and learning algorithms for ordinal regression problems
Information Fusion
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
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In this paper, we address the problem of learning the order of a group of items. This study assumes that items have unobserved “scores” and are ranked by the score. The goal is to derive an approximate scoring function given ordered lists of items without their scores. We extend the notion of order statistics to any order and prove that the distribution functions of the extended statistics are scoring functions. We then propose a learning algorithm, Order SVM, that can approximate the distribution functions. Order SVM is experimentally compared with other learning algorithms using artificial data and important sentence selection data. The results show that Order SVM performs well on both datasets whereas the other methods show good performance on only one of the datasets. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 36(1): 35–43, 2005; Published online in Wiley InterScience (). DOI 10.1002/scj.10630