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A new modification of the explanation method SurvLIME called SurvLIME-Inf for explaining machine learning survival models is proposed.
Regression models and life-tables
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Prospective evaluation of prognostic variables from patient-completed questionnaires. north central cancer treatment group
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A modified perturbed sampling method for local interpretable model-agnostic explanation
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Development and validation of a prognostic model for survival time data: application to prognosis of HIV positive patients treated with antiretroviral therapy
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A probabilistic approach to the geometry of the l-ball
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Generating survival times to simulate cox proportional hazards models
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Dnnsurv: Deep neural networks for survival analysis using pseudo values
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Removing outliers using the l ∞ l_{\infty} norm
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Adaptive Lasso for Cox’s proportional hazards model
H.H. Zhang and W. Lu · 2007
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Applied Survival Analysis: Regression Modeling of Time to Event Data
D. Hosmer, S. Lemeshow, and S. May · 2008
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On decompositional algorithms for uniform sampling from n-spheres and n-balls
R. Harman and V. Lacko · 2010
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An efficient explanation of individual classifications using game theory
E. Strumbel and I. Kononenko · 2010
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A review of survival trees
I. Bou-Hamad, D. Larocque, and H. Ben-Ameur · 2011
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Analysis of survival data with group lasso
J. Kim, I. Sohn, S.-H. Jung, S. Kim, and C. Park · 2012
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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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Interpretable explanations of black boxes by meaningful perturbation
R.C. Fong and A. Vedaldi · 2017
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What do we need to build explainable ai systems for the medical domain?
Analysis of explainers of black box deep neural networks for computer vision: A survey
V. Buhrmester, D. Munch, and M. Arens · 2019
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Machine learning interpretability: A survey on methods and metrics
D.V. Carvalho, E.M. Pereira, and J.S. Cardoso · 2019
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Techniques for interpretable machine learning
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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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, C. Biemann, C.S. Pattichis, and D.B. Kell · 2017
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A unified approach to interpreting model predictions
S.M. Lundberg and S.-I. Lee · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GPDR
S. Wachter, B. Mittelstadt, and C. Russell · 2017
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Machine learning for survival analysis: A survey
P. Wang, Y. Li, and C.K. Reddy · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
A. Adadi and M. Berrada · 2018
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Locally interpretable models and effects based on supervised partitioning (LIME-SUP)
L. Hu, J. Chen, V.N. Nair, and A. Sudjianto · 2018
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Deephit: A deep learning approach to survival analysis with competing risks
C. Lee, W.R. Zame, J. Yoon, and M. van der Schaar · 2018
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A modified lime and its application to explain service supply chain forecasting
H. Li, W. Fan, S. Shi, and Q. Chou · 2019
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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
C. Molnar · 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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A survey on explainable artificial intelligence (XAI): towards medical XAI
E. Tjoa and C. Guan · 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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