Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

5 November 2015

Papers citing "Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model"

50 / 244 papers shown

Title
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson Limor Gultchin Ankur Taly Luciano Floridi 81 64 0 27 Mar 2021
Robust subgroup discovery Hugo Manuel Proença Peter Grünwald Thomas Bäck M. Leeuwen 54 13 0 25 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 245 677 0 20 Mar 2021
CACTUS: Detecting and Resolving Conflicts in Objective Functions Subhajit Das Alex Endert 53 0 0 13 Mar 2021
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks Litao Qiao Weijia Wang Bill Lin FaML 34 40 0 04 Mar 2021
Visualizing Rule Sets: Exploration and Validation of a Design Space Jun Yuan O. Nov E. Bertini 44 1 0 01 Mar 2021
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence Atoosa Kasirzadeh 70 24 0 01 Mar 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 127 234 0 25 Feb 2021
3D4ALL: Toward an Inclusive Pipeline to Classify 3D Contents Nahyun Kwon Chen Liang Jeeeun Kim DiffM 36 1 0 24 Feb 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations Sushant Agarwal S. Jabbari Chirag Agarwal Sohini Upadhyay Zhiwei Steven Wu Himabindu Lakkaraju FAtt AAML 82 64 0 21 Feb 2021
VitrAI -- Applying Explainable AI in the Real World Marc Hanussek Falko Kötter Maximilien Kintz Jens Drawehn 37 2 0 12 Feb 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond Guang Yang Qinghao Ye Jun Xia 141 504 0 03 Feb 2021
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations Danding Wang Wencan Zhang Brian Y. Lim FAtt 56 22 0 23 Jan 2021
Dissonance Between Human and Machine Understanding Zijian Zhang Jaspreet Singh U. Gadiraju Avishek Anand 125 74 0 18 Jan 2021
Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction Yi Wei 104 1 0 10 Jan 2021
Learning Interpretable Concept-Based Models with Human Feedback Isaac Lage Finale Doshi-Velez 49 25 0 04 Dec 2020
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 77 782 0 16 Nov 2020
Robust and Stable Black Box Explanations Himabindu Lakkaraju Nino Arsov Osbert Bastani AAML FAtt 65 84 0 12 Nov 2020
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making S. McGrath Parth Mehta Alexandra Zytek Isaac Lage Himabindu Lakkaraju UD 68 26 0 12 Nov 2020
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions Kasun Amarasinghe Kit Rodolfa Hemank Lamba Rayid Ghani ELM XAI 187 53 0 27 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 114 405 0 19 Oct 2020
A general approach to compute the relevance of middle-level input features Andrea Apicella Salvatore Giugliano Francesco Isgrò R. Prevete 49 6 0 16 Oct 2020
Learning Binary Decision Trees by Argmin Differentiation Valentina Zantedeschi Matt J. Kusner Vlad Niculae 62 13 0 09 Oct 2020
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification Florentin Flambeau Jiechieu Kameni Norbert Tsopzé XAI FAtt MedIm 26 1 0 08 Oct 2020
Explainability via Responsibility Faraz Khadivpour Matthew J. Guzdial 34 2 0 04 Oct 2020
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing Ping Lang Xiongjun Fu M. Martorella Jian Dong Rui Qin Xianpeng Meng M. Xie 41 42 0 29 Sep 2020
Explainable Predictive Process Monitoring Musabir Musabayli F. Maggi Williams Rizzi Josep Carmona Chiara Di Francescomarino 68 61 0 04 Aug 2020
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution A. Khademi Vasant Honavar CML 39 9 0 01 Aug 2020
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification Cristina Garbacea Mengtian Guo Samuel Carton Qiaozhu Mei 60 28 0 31 Jul 2020
Event Prediction in the Big Data Era: A Systematic Survey Liang Zhao AI4TS 102 56 0 19 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Vladimiro González-Zelaya P. Missier Magdalene Ng Aad van Moorsel 74 18 0 17 Jul 2020
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing Max Biggs Wei-Ju Sun M. Ettl 75 35 0 03 Jul 2020
Causality Learning: A New Perspective for Interpretable Machine Learning Guandong Xu Tri Dung Duong Q. Li S. Liu Xianzhi Wang XAI OOD CML 62 52 0 27 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 179 607 0 16 Jun 2020
A Formal Language Approach to Explaining RNNs Bishwamittra Ghosh Daniel Neider 40 1 0 12 Jun 2020
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts R. Akrour Davide Tateo Jan Peters 60 22 0 10 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 110 271 0 29 May 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 46 5 0 20 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 139 101 0 05 May 2020
Interpretable Random Forests via Rule Extraction Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet 47 59 0 29 Apr 2020
Valid Explanations for Learning to Rank Models Jaspreet Singh Zhenye Wang Megha Khosla Avishek Anand LRM FAtt 43 8 0 29 Apr 2020
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs Sungsoo Ray Hong Jessica Hullman E. Bertini HAI 94 197 0 23 Apr 2020
A New Method to Compare the Interpretability of Rule-based Algorithms Vincent Margot G. Luta FAtt 20 17 0 03 Apr 2020
A Hierarchy of Limitations in Machine Learning M. Malik 68 57 0 12 Feb 2020
Leveraging Rationales to Improve Human Task Performance Devleena Das Sonia Chernova 77 50 0 11 Feb 2020
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 51 8 0 20 Jan 2020
Interpretation and Simplification of Deep Forest Sangwon Kim Mira Jeong ByoungChul Ko FAtt 104 8 0 14 Jan 2020
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules Bishwamittra Ghosh Kuldeep S. Meel 75 35 0 07 Jan 2020
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization Zhuo Wang Wei Zhang Ning Liu Jianyong Wang 44 30 0 10 Dec 2019
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey Vanessa Buhrmester David Münch Michael Arens MLAU FaML XAI AAML 117 368 0 27 Nov 2019