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A new robust algorithm based of the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models.
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C.J. Mantas and J. Abellan · 2014
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A framework for imprecise robust one-class classification models
L.V. Utkin · 2014
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Classification with support vector machines and Kolmogorov-Smirnov bounds
L.V. Utkin and F.P.A. Coolen · 2014
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Reliable survival analysis based on the Dirichlet process
F. Mangili, A. Benavoli, C.P. de Campos, and M. Zaffalon · 2015
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Improving over-fitting in ensemble regression by imprecise probabilities
L.V. Utkin and A. Wiencierz · 2015
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R. Ranganath, A. Perotte, N. Elhadad, and D. Blei · 2016
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“Why should I trust You?” Explaining the predictions of any classifier
M.T. Ribeiro, S. Singh, and C. Guestrin · 2016
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M. Du, N. Liu, and X. Hu · 2019
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Explanations for attributing deep neural network predictions
R. Fong and A. Vedaldi · 2019
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Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, and S. Lee · 2019
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A survey of methods for explaining black box models
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi · 2019
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A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Muller · 2019
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A. Van Looveren and J. Klaise · 2019
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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
C. Molnar · 2019
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Learning with imprecise probabilities as model selection and averaging
S. Moral · 2019
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Interpretable machine learning: definitions, methods, and applications
W.J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yua · 2019
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Enriching visual with verbal explanations for relational concepts: Combining LIME with Aleph
J. Rabold, H. Deininger, M. Siebers, and U. Schmid · 2019
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Counterfactual explanation algorithms for behavioral and textual data
Y. Ramon, D. Martens, F. Provost, and T. Evgeniou · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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ALIME: Autoencoder based approach for local interpretability
S.M. Shankaranarayana and D. Runje · 2019
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An imprecise extension of svm-based machine learning models
L.V. Utkin · 2019
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Robust regression random forests by small and noisy training data
L.V. Utkin, M.S. Kovalev, and F. Coolen · 2019
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Imprecise extensions of random forests and random survival forests
L.V. Utkin, M.S. Kovalev, A.A. Meldo, and F.P.A. Coolen · 2019
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Evaluating explainers via perturbation
M.N. Vu, T.D. Nguyen, N. Phan, and M.T. Thai R. Gera · 2019
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Measurable counterfactual local explanations for any classifier
A. White and A.dA. Garcez · 2019
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M.R. Zafar and N.M. Khan · 2019
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Explaining the explainer: A first theoretical analysis of LIME
D. Garreau and U. von Luxburg · 2020
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GraphLIME: Local interpretable model explanations for graph neural networks
Q. Huang, M. Yamada, Y. Tian, D. Singh, D. Yin, and Y. Chang · 2020
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SurvLIME: A method for explaining machine learning survival models
M.S. Kovalev, L.V. Utkin, and E.M. Kasimov · 2020
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Bagging of credal decision trees for imprecise classification
S. Moral-Garcia, C.J. Mantas, J.G. Castellano, M.D. Benitez, and J. Abellan · 2020
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An imprecise deep forest for classification
L.V. Utkin · 2020
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Explainable deep learning: A field guide for the uninitiated
N. Xie, G. Ras, M. van Gerven, and D. Doran · 2020
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Dnnsurv: Deep neural networks for survival analysis using pseudo values
L. Zhao and D. Feng · 2020
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