Learning to rank order — a distance-based approach
Learning to rank order is a machine learning paradigm that is different to the common machine learning paradigms: learning to classify cluster or approximate. It has the potential to reveal more hidden knowledge in data than classification. Cohen, Schapire and Singer were early investigators of this problem. They took a preference-based approach where pairwise preferences were combined into a total ordering. It is however not always possible to have knowledge of pairwise preferences. In this paper we consider a distance-based approach to ordering, where the ordering of alternatives is predicted on the basis of their distances to a query. To learn such an ordering function we consider two orderings: one is the actual ordering and another one is the predicted ordering. We aim to maximise the agreement of the two orderings by varying the parameters of a distance function, resulting in a trained distance function which is taken to be the ordering function. We evaluated this work by comparing the trained distance and the untrained distance in an experiment on public data. Results show that the trained distance leads in general to a higher degree of agreement than untrained distance.
KeywordsAttribute Weight Simple Genetic Algorithm Actual Ranking Ranking Distance Query Vector
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- 1.Asuncion, A., Newman, D.: UCI machine learning repository (2007). URL http://www.ics.uci.edu/~mlearn/MLRepository.htmlGoogle Scholar
- 2.Chen, J., Zhao, Z., Ye, J., Liu, H.: Nonlinear adaptive distance metric learning for clustering. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD07), pp. 123–132. ACM (2007)Google Scholar
- 3.Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. In: Advances in Neural Information Processing Systems, vol. 10. The MIT Press (1998)Google Scholar
- 4.Fentress, S.W.: Exaptation as a means of evolving complex solutions (2005). MSc thesisGoogle Scholar
- 6.Hochberg, Y., Rabinovitch, R.: Ranking by pairwise comparisons with special reference to ordering portfolios. American Journal of Mathematical and Management Sciences 20 (2000)Google Scholar
- 9.Schultz, M., Joachims, T.: Learning a distance metric from relative comparisons. In: Proceedings of Neural Information Processing Systems (NIPS-04) (2004)Google Scholar
- 10.Wang, H.: All common subsequences. In: Proceedings of International Joint Conference in Artificial Intelligence (IJCAI-07), pp. 635–640 (2007)Google Scholar
- 12.Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)Google Scholar