Abstract
We are living in an era that we can call machine learning revolution. Started as a pure academic and research-oriented domain, we have seen widespread commercial adoption across diverse domains, such as retail, healthcare, finance, and many more. However, the usage of machine learning poses its own set of challenges when it comes to explain what is going on under the hood. The reason being models interpretability is very important for the business is to explain each and every decision being taken by the model. In order to take a step forward in this direction, we propose a principled algorithm inspired by both preference learning and game theory for classification. Particularly, the learning problem is posed as a two player zero-sum game which we show having theoretical guarantees about its convergence. Interestingly, feature selection can be straightforwardly plugged into such algorithm. As a consequence, the hypotheses space consists on a set of preference prototypes along with (possibly non-linear) features making the resulting models easy to interpret.
Keywords
- Game theory
- Margin maximization
- Classification
- Preference learning
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Aiolli, F., Sperduti, A.: A preference optimization based unifying framework for supervised learning problems. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 19–42. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14125-6_2
Brown, G.W.: Iterative solutions of games by fictitious play. In: Activity Analysis of Production and Allocation, pp. 374–376 (1951)
Freund, Y., Schapire, R.E.: Game theory, on-line prediction and boosting. In: COLT, pp. 325–332 (1996)
Freund, Y., Schapire, R.E.: Adaptive game playing using multiplicative weights. Games Econ. Behav. 29(1–2), 79–103 (1999)
Fürnkranz, J., Hüllermeier, E.: Preference Learning, 1st edn. Springer, Heidelberg (2010). https://doi.org/10.1007/978-0-387-30164-8
Guyon, I., Gunn, S., Ben-Hur, A., Dror, G.: Result analysis of the nips 2003 feature selection challenge. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 545–552. MIT Press, Cambridge (2005)
Hofmann, T., Schlkopf, B., Smola, A.J.: Kernel methods in machine learning. The Ann. Stat. 36(3), 1171–1220 (2008)
Johnson, N.: A study of the nips feature selection challenge (2009). https://web.stanford.edu/~hastie/ElemStatLearn/comp.pdf
Kimeldorf, G.S., Wahba, G.: Some results on Tchebycheffian spline functions. J. Math. Anal. Appl. 33(1), 82–95 (1971)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
von Neumann, J.: Zur theorie der gesellschaftsspiele. Math. Ann. 100, 295–320 (1928)
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Polato, M., Aiolli, F. (2018). A Game-Theoretic Framework for Interpretable Preference and Feature Learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_65
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DOI: https://doi.org/10.1007/978-3-030-01418-6_65
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