Abstract
Learning to rank based on principles of analogical reasoning has recently been proposed as a novel method in the realm of preference learning. Roughly speaking, the method proceeds from a regularity assumption as follows: Given objects A, B, C, D, if A relates to B as C relates to D, and A is preferred to B, then C is presumably preferred to D. This assumption is formalized in terms of so-called analogical proportions, which operate on a feature representation of the objects. Consequently, a suitable feature representation is an important prerequisite for the success of analogy-based learning to rank. In this paper, we therefore address the problem of feature selection and adapt common feature selection techniques, including forward selection, correlation-based filter techniques, as well as Relief-based methods, to the case of analogical learning. The usefulness of these approaches is shown in experiments with synthetic and benchmark data.
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Actually, we do not produce a ranking of all features, but include only those features whose scores are positive.
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The description is available at https://github.com/mahmadif/able2rank.
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Ahmadi Fahandar, M., Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_22
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DOI: https://doi.org/10.1007/978-3-030-33778-0_22
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