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Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models.
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Berger, A., Korkut, A., Kanchi, R.S., Group, T.P.G., Mills, G.B., Levine, D.A., Akbani, R.: A comprehensive tcga pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell 33
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Sundararajan, M., Najmi, A.: The many shapley values for model explanation. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol. 119, pp. 9269–9278. PMLR (2020), http://proceedings.mlr.press/v119/sundararajan20b.html
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Tomita, T.M., Browne, J., Shen, C., Chung, J., Patsolic, J.L., Falk, B., Priebe, C.E., Yim, J., Burns, R., Maggioni, M., et al.: Sparse projection oblique randomer forests. The Journal of Machine Learning Research 21
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Aas, K., Jullum, M., Løland, A.: Explaining individual predictions when features are dependent: More accurate approximations to shapley values. Artif. Intell. 298
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2018
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2018
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Berk, R.: Machine Learning Risk Assessments in Criminal Justice Settings. Springer (2019)
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Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1
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2020
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Ferdous, M., Debnath, J., Chakraborty, N.R.: Machine learning algorithms in healthcare: A literature survey. In: 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, Kharagpur, India, July 1-3, 2020. pp. 1–6. IEEE (2020)
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2021
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Hooker, G., Mentch, L., Zhou, S.: Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance. Statistics and Computing 31
2021
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Zhou, Z., Hooker, G., Wang, F.: S-LIME: stabilized-lime for model explanation. In: Zhu, F., Ooi, B.C., Miao, C. (eds.) KDD ’21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021. pp. 2429–2438. ACM (2021). https://doi.org/10.1145/3447548.3467274, https://doi.org/10.1145/3447548.3467274
2021
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Campbell, T.W., Roder, H., Georgantas III, R.W., Roder, J.: Exact shapley values for local and model-true explanations of decision tree ensembles. Machine Learning with Applications 9
2022
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Dubey, R., Chandani, A.: Application of machine learning in banking and finance: a bibliometric analysis. Int. J. Data Anal. Tech. Strateg. 14
2022
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Goli, A., Mohammadi, H.: Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain. Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development 24
2022
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Jethani, N., Sudarshan, M., Covert, I.C., Lee, S., Ranganath, R.: Fastshap: Real-time shapley value estimation. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net (2022)
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Mitchell, R., Cooper, J., Frank, E., Holmes, G.: Sampling permutations for shapley value estimation. J. Mach. Learn. Res. 23
2022
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Mandalapu, V., Elluri, L., Vyas, P., Roy, N.: Crime prediction using machine learning and deep learning: A systematic review and future directions. IEEE Access 11
2023
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