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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Actually, we do not produce a ranking of all features, but include only those features whose scores are positive.
- 2.
- 3.
The description is available at https://github.com/mahmadif/able2rank.
References
Ahmadi Fahandar, M., Hüllermeier, E.: Learning to rank based on analogical reasoning. In: AAAI (2018)
Ahmadi Fahandar, M., Hüllermeier, E.: Analogy-based preference learning with kernels. In: Benzmüller, C., Stuckenschmidt, H. (eds.) KI 2019. LNCS (LNAI), vol. 11793, pp. 34–47. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30179-8_3
Ahmadi Fahandar, M., Hüllermeier, E., Couso, I.: Statistical inference for incomplete ranking data: the case of rank-dependent coarsening. In: ICML (2017)
Bounhas, M., Pirlot, M., Prade, H.: Predicting preferences by means of analogical proportions. In: ICCBR (2018)
Draper, B., Kaito, C., Bins, J.: Iterative relief. In: 2003 Conference on Computer Vision and Pattern Recognition Workshop (2003)
Fürnkranz, J., Hüllermeier, E.: Preference Learning. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-14125-6
Geng, Z., Shi, N.Z.: Algorithm AS 257: isotonic regression for umbrella orderings. J. R. Stat. Soc. Seri. C (Appl. Stat.) 39(3), 397–402 (1990)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. JMLR 3, 1157–1182 (2003)
Keogh, E.: Instance-Based Learning, pp. 549–550. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_409
Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new algorithm. In: AAAI (1992)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings ML-92, 9th International Workshop on Machine Learning (1992)
Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: ECML (1994)
Miclet, L., Prade, H.: Handling analogical proportions in classical logic and fuzzy logics settings. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS (LNAI), vol. 5590, pp. 638–650. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02906-6_55
Mirzazadeh, F., Guo, Y., Schuurmans, D.: Convex co-embedding. In: AAAI (2014)
Sun, Y.: Iterative relief for feature weighting: algorithms, theories, and applications. IEEE TPAMI 29(6), 1035–1051 (2007)
Sun, Y., Li, J.: Iterative relief for feature weighting. In: ICML (2006)
Turner, T., Wollan, P.: Locating a maximum using isotonic regression. Comput. Stat. Data Anal. 25(3), 305–320 (1997)
Urbanowicz, R., Meeker, M., LaCava, W., Olson, R., Moore, J.: Relief-based feature selection: introduction and review. J. Biomed. Inform. 85, 189–203 (2017)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. JMLR 10, 207–244 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-33778-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33777-3
Online ISBN: 978-3-030-33778-0
eBook Packages: Computer ScienceComputer Science (R0)