=Paper= {{Paper |id=Vol-1917/paper02 |storemode=property |title=None |pdfUrl=https://ceur-ws.org/Vol-1917/paper02.pdf |volume=Vol-1917 }} ==None== https://ceur-ws.org/Vol-1917/paper02.pdf 
                             Shapley Curves: A New Concept for
                               Modelling Feature Importance

                               Farjad Adnan, Karlson Pfannschmidt, Eyke Hüllermeier

                                                  Intelligent Systems Group
                                                    Paderborn University

                     We propose a novel method for measuring the importance and usefulness of
                 predictor variables (features) in supervised machine learning, which makes use
                 of concepts from cooperative game theory. The basic idea of our approach is to
                 consider subsets of variables as coalitions, and their predictive performance as a
                 payoff. This approach acknowledges the fact that the usefulness of a feature in a
                 learning context strongly depends, not only on the learning method being used,
                 but also on the other features being available.
                     A theoretically appealing measure of the importance of an individual feature
                 is the Shapley value [3]. Computationally, however, this measure is challenging.
                 First, the exact computation of the Shapley values requires determining the
                 performance of all possible subsets of features, which is in general #P-hard [2].
                 Furthermore, in the context of machine learning, even the training of a single
                 predictor on one subset of features can take a considerable amount of time.
                     As another aspect specific to machine learning, let us note that the Shapley
                 values of each feature can change with varying sample size, due to effects such as
                 overfitting. Motivated by this observation, we introduce the concept of a Shapley
                 curve, which depicts the (weighted average) contribution of a feature to the
                 learning curve (expected performance as a function of the sample size).
                     We develop an approximation technique for estimating Shapley values, which
                 is efficient in the number of models that need to be trained and validated.
                 Moreover, to estimate Shapley curves, we propose a hierarchical Bayes approach
                 that does not require an evaluation of all possible subsets of features on different
                 sample sizes. Last but not least, leveraging related techniques for extrapolating
                 learning curves [1], we are able to estimate the Shapley values in the limit when
                 the sample size goes to infinity. We evaluate our approach on synthetic and
                 real-world datasets.

                 References
                 1. C. Cortes, L.D. Jackel, S.A. Solla, V. Vapnik, and J.S. Denker. Learning curves:
                    Asymptotic values and rate of convergence. In Proc. NIPS, Advances in Neural
                    Information Processing Systems, Denver, USA, 1993.
                 2. X. Deng and C.H. Papadimitriou. On the complexity of cooperative solution concepts.
                    Math. Oper. Res., 19(2):257–266, 1994.
                 3. K. Pfannschmidt, E. Hüllermeier, S. Held, and R. Neiger. Evaluating tests in medical
                    diagnosis: Combining machine learning with game-theoretical concepts. In Proc.
                    IPMU, International Conference on Information Processing and Management of
                    Uncertainty in Knowledge-Based Systems, Eindhoven, The Netherlands, 2016.




Copyright © 2017 by the paper’s authors. Copying permitted only for private and academic purposes.
In: M. Leyer (Ed.): Proceedings of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB.
Rostock, Germany, 11.-13. September 2017, published at http://ceur-ws.org