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Shapley values are one of the main tools used to explain predictions of tree ensemble models.
A Value for n-Person Games
L. S. Shapley · 1953
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Mathematical properties of the banzhaf power index
P. Dubey and L. S. Shapley · 1979
Earlier work this paper cites.
Classification and Regression Trees
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone · 1984
Earlier work this paper cites.
An axiomatization of the banzhaf value
E. Lehrer · 1988
Earlier work this paper cites.
The shapley value : essays in honor of Lloyd S. Shapley
A. Roth · 1988
Earlier work this paper cites.
Probabilistic values for games
R. J. Weber · 1988
Earlier work this paper cites.
Shapley Value
S. Hart · 1989
Earlier work this paper cites.
NP-completeness of some problems concerning voting games
K. Prasad and J. S. Kelly · 1990
Earlier work this paper cites.
On the complexity of cooperative solution concepts
X. Deng and C. H. Papadimitriou · 1994
Earlier work this paper cites.
On the choice of a power index
A. Laruelle · 1999
Earlier work this paper cites.
Greedy function approximation: A gradient boosting machine
J. H. Friedman · 2001
Earlier work this paper cites.
Shapley-Shubik and Banzhaf Indices Revisited
A. Laruelle and F. Valenciano · 2001
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Analysis of regression in game theory approach
S. Lipovetsky and M. Conklin · 2001
Earlier work this paper cites.
NP-completeness for calculating power indices of weighted majority games
Y. Matsui and T. Matsui · 2001
Earlier work this paper cites.
An empirical comparison of the performance of classical power indices
D. Leech · 2002
Earlier work this paper cites.
Power: A philosophical analysis, 2nd edition
K. Dowding · 2003
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Random forests
L. Breiman · 2004
Earlier work this paper cites.
Feature deduction and ensemble design of intrusion detection systems
S. Chebrolu, A. Abraham, and J. Thomas · 2005
Cited alongside, same era.
On the performance of the Shapley Shubik and Banzhaf power indices for the allocations of mandates
F. Barthélémy, M. Martin, and V. Merlin · 2007
Cited alongside, same era.
A bias correction algorithm for the gini variable importance measure in classification trees
M. Sandri and P. Zuccolotto · 2008
Cited alongside, same era.
Measuring Voting Power: The Paradox of New Members vs. the Null Player Axiom
L. Á. Kóczy · 2009
Cited alongside, same era.
How to explain individual classification decisions
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller · 2010
Cited alongside, same era.
Inferring regulatory networks from expression data using tree-based methods
Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2018
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Learning how to explain neural networks: Patternnet and patternattribution
P. Kindermans, K. T. Schütt, M. Alber, K. Müller, D. Erhan, B. Kim, and S. Dähne · 2018
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Consistent individualized feature attribution for tree ensembles
S. M. Lundberg, G. G. Erion, and S.-I. Lee · 2018
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Model agnostic supervised local explanations
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V. A. Huynh-Thu, A. Irrthum, L. Wehenkel, and P. Geurts · 2010
Cited alongside, same era.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
Explaining prediction models and individual predictions with feature contributions
E. Štrumbelj and I. Kononenko · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K. Müller, and W. Samek · 2015
Cited alongside, same era.
Influence in classification via cooperative game theory
A. Datta, A. Datta, A. D. Procaccia, and Y. Zick · 2015
Cited alongside, same era.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2015
Cited alongside, same era.
G. Plumb, D. Molitor, and A. S. Talwalkar · 2018
Later among the works it cites.
Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
Later among the works it cites.
On the ordinal equivalence of the jonhston, banzhaf and shapley–shubik power indices for voting games with abstention
J. A. Momo Kenfack, B. Tchantcho, and B. P. Tsague · 2019
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Multimodal detection of parkinson disease based on vocal and improved spiral test
H. N. Pham, T. T. Do, K. Y. J. Chan, G. Sen, A. Han, P. Lim, T. S. L. Cheng, Q. H. Nguyen, B. P. Nguyen, and M. C. H. Chua · 2019
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Axiomatic characterization of data-driven influence measures for classification
J. Sliwinski, M. Strobel, and Y. Zick · 2019
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Feature relevance quantification in explainable AI: A causal problem
D. Janzing, L. Minorics, and P. Blöbaum · 2020
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From local explanations to global understanding with explainable AI for trees
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee · 2020
Later among the works it cites.
High dimensional model explanations: an axiomatic approach, 2020
N. Patel, M. Strobel, and Y. Zick · 2020
Later among the works it cites.
The many shapley values for model explanation
M. Sundararajan and A. Najmi · 2020
Later among the works it cites.
A novel method with stacking learning of data-driven soft sensors for mud concentration in a cutter suction dredger
B. Wang, S.-d. Fan, P. Jiang, H.-h. Zhu, T. Xiong, W. Wei, and Z.-l. Fang · 2020
Later among the works it cites.
Towards fast, routine blood sample quality evaluation by probe electrospray ionization (pesi) metabolomics
N. Bordag, E. Zügner, P. López-García, S. Kofler, M. Tomberger, A. Al-Baghdadi, J. Schweiger, Y. Erdem, C. Magnes, S. Hidekazu, W. Wadsak, B.-T. Erxleben, and B. Prietl · 2021
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