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In recent years, there has been significant work on increasing both interpretability and debuggability of a Deep Neural Network (DNN) by extracting a rule-based model that approximates its decision boundary.
Stochastic estimation of the maximum of a regression function
Jack Kiefer, Jacob Wolfowitz, et al · 1952
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Rule learning by searching on adapted nets
LiMin Fu · 1991
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Letter recognition using Holland-style adaptive classifiers
Peter W Frey and David J Slate · 1991
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Multivariate versus univariate decision trees
Carla E Brodley and Paul E Utgoff · 1992
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Extracting refined rules from knowledge-based neural networks
Geoffrey G Towell and Jude W Shavlik · 1993
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Extracting refined rules from knowledge-based neural networks
Geoffrey G Towell and Jude W Shavlik · 1993
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Rule generation from neural networks
LiMin Fu · 1994
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Rule extraction from a constrained error back propagation mlp
Robert Andrews and Shlomo Geva · 1994
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Survey and critique of techniques for extracting rules from trained artificial neural networks
Robert Andrews, Joachim Diederich, and Alan B Tickle · 1995
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Extracting rules from artificial neural networks with distributed representations
Sebastian Thrun · 1995
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Inserting and extracting knowledge from constrained error back-propagation networks
Robert Andrews · 1995
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Random decision forests
Tin Kam Ho · 1995
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Machine learning bias, statistical bias, and statistical variance of decision tree algorithms
Thomas G Dietterich and Eun Bae Kong · 1995
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Extracting comprehensible models from trained neural networks
Mark W Craven · 1996
Earlier work this paper cites.
Rule extraction from neural networks via decision tree induction
Makoto Sato and Hiroshi Tsukimoto · 2001
Earlier work this paper cites.
Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach
Eduardo R Hruschka and Nelson FF Ebecken · 2006
Earlier work this paper cites.
Neural network explanation using inversion
Emad W Saad and Donald C Wunsch II · 2007
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Major Atmospheric Gamma Imaging Cherenkov Telescope project (MAGIC)
RK Bock · 2007
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UCI machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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A comparison of different off-centered entropies to deal with class imbalance for decision trees
Philippe Lenca, Stéphane Lallich, Thanh-Nghi Do, and Nguyen-Khang Pham · 2008
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The MiniBooNE detector
AA Aguilar-Arevalo, CE Anderson, LM Bartoszek, AO Bazarko, SJ Brice, BC Brown, L Bugel, J Cao, L Coney, JM Conrad, et al · 2009
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Classification and regression trees
Wei-Yin Loh · 2011
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Leakage in data mining: Formulation, detection, and avoidance
Shachar Kaufman, Saharon Rosset, Claudia Perlich, and Ori Stitelman · 2012
Cited alongside, same era.
RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry
Adam M Smith, Wen Xu, Yao Sun, James R Faeder, and G Elisabeta Marai · 2012
Cited alongside, same era.
Too much, too little, or just right? ways explanations impact end users’ mental models
Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong · 2013
Cited alongside, same era.
C4. 5: programs for machine learning
J Ross Quinlan · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Classification and regression trees
Leo Breiman, Jerome H Friedman, Richard A Olshen, and Charles J Stone · 2017
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Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda
Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y Lim, and Mohan Kankanhalli · 2018
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Autonomous vehicles: No driver… no regulation?
Joan Claybrook and Shaun Kildare · 2018
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The uber crash won’t be the last shocking self-driving death
Aarian Marshall · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John E Hopcroft · 2015
Cited alongside, same era.
Rule extraction from random forest: the rf+ hc methods
Morteza Mashayekhi and Robin Gras · 2015
Cited alongside, same era.
American joint committee on cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine
Michael W Kattan, Kenneth R Hess, Mahul B Amin, Ying Lu, Karl GM Moons, Jeffrey E Gershenwald, Phyllis A Gimotty, Justin H Guinney, Susan Halabi, Alexander J Lazar, et al · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Rule extraction algorithm for deep neural networks: A review
Tameru Hailesilassie · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Oluwasanmi Koyejo, Rajiv Khanna, et al · 2016
Cited alongside, same era.
Deepred–rule extraction from deep neural networks
Jan Ruben Zilke, Eneldo Loza Mencía, and Frederik Janssen · 2016
Cited alongside, same era.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Later among the works it cites.
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, et al · 2018
Later among the works it cites.
RuleMatrix: Visualizing and understanding classifiers with rules
Yao Ming, Huamin Qu, and Enrico Bertini · 2018
Later among the works it cites.
Local rule-based explanations of black box decision systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti · 2018
Later among the works it cites.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Later among the works it cites.
Boolean decision rules via column generation
Sanjeeb Dash, Oktay Günlük, and Dennis Wei · 2018
Later among the works it cites.
Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan · 2018
Later among the works it cites.
Demystifying black-box models with symbolic metamodels
Ahmed M Alaa and Mihaela van der Schaar · 2019
Later among the works it cites.
Top-down induction of decision trees: rigorous guarantees and inherent limitations
Guy Blanc, Jane Lange, and Li-Yang Tan · 2019
Later among the works it cites.
Extract interpretability-accuracy balanced rules from artificial neural networks: A review
Congjie He, Meng Ma, and Ping Wang · 2020
Later among the works it cites.
Multi-objective counterfactual explanations
Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl · 2020
Later among the works it cites.
Data mining tools see5 and c5.0
J Ross Quinlan · 2020
Later among the works it cites.
Optimising rule extraction for deep neural networks
Sumaiyah Kola · 2020
Later among the works it cites.
Rule extraction from neural network trained using deep belief network and back propagation
Manomita Chakraborty, Saroj Kumar Biswas, and Biswajit Purkayastha · 2020
Later among the works it cites.
A survey on the explainability of supervised machine learning
Nadia Burkart and Marco F Huber · 2021
Closest in time.
REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
Zohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski, Helena Andres Terre, Paul Scherer, Urska Matjasec, Jean Abraham, Mateja Jamnik, and Pietro Lio · 2021
Closest in time.