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Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models.
Optimal binary identification procedures
M. R. Garey · 1972
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Constructing optimal binary decision trees is np-complete
L. Hyafil and R. L. Rivest · 1976
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Decision trees and diagrams
B. M. E. Moret · 1982
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Classification and Regression Trees
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone · 1984
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Induction of decision trees
J. R. Quinlan · 1986
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Learning decision lists
R. L. Rivest · 1987
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Multivariate decision trees
C. E. Brodley and P. E. Utgoff · 1995
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Simplifying decision trees: A survey
L. A. Breslow and D. W. Aha · 1997
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Machine learning
T. M. Mitchell · 1997
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Decision tree induction based on efficient tree restructuring
P. E. Utgoff, N. C. Berkman, and J. A. Clouse · 1997
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Statistical modeling: The two cultures
L. Breiman · 2001
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Top-down induction of decision trees classifiers - a survey
L. Rokach and O. Maimon · 2005
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Mining optimal decision trees from itemset lattices
S. Nijssen and É. Fromont · 2007
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Data Mining with Decision Trees - Theory and Applications
L. Rokach and O. Maimon · 2007
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Minimising decision tree size as combinatorial optimisation
C. Bessiere, E. Hebrard, and B. O’Sullivan · 2009
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Optimal constraint-based decision tree induction from itemset lattices
S. Nijssen and É. Fromont · 2010
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Artificial Intelligence - A Modern Approach
S. J. Russell and P. Norvig · 2010
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Network coding for computing: Cut-set bounds
R. Appuswamy, M. Franceschetti, N. Karamchandani, and K. Zeger · 2011
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Scikit-learn: Machine learning in python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. VanderPlas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Machine Learning - The Art and Science of Algorithms that Make Sense of Data
P. A. Flach · 2012
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Ensemble methods: foundations and algorithms
Z.-H. Zhou · 2012
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Comprehensible classification models: a position paper
A. A. Freitas · 2013
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Decision trees: a recent overview
S. B. Kotsiantis · 2013
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An interpretable stroke prediction model using rules and bayesian analysis
B. Letham, C. Rudin, T. H. McCormick, and D. Madigan · 2013
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Falling rule lists
F. Wang and C. Rudin · 2015
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Machine Learning: The New AI
E. Alpaydin · 2016
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Principles of Data Mining, Third Edition
M. Bramer · 2016
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Interpretable decision sets: A joint framework for description and prediction
H. Lakkaraju, S. H. Bach, and J. Leskovec · 2016
Cited alongside, same era.
Model-agnostic interpretability of machine learning
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Mediboost: a patient stratification tool for interpretable decision making in the era of precision medicine
G. Valdes, J. M. Luna, E. Eaton, C. B. Simone II, L. H. Ungar, and T. D. Solberg · 2016
Cited alongside, same era.
Bayesian rule sets for interpretable classification
T. Wang, C. Rudin, F. Doshi-Velez, Y. Liu, E. Klampfl, and P. MacNeille · 2016
Cited alongside, same era.
Learning certifiably optimal rule lists
E. Angelino, N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin · 2017
Cited alongside, same era.
Abduction-based explanations for machine learning models
A. Ignatiev, N. Narodytska, and J. Marques-Silva · 2019
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Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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Interpretable Machine Learning
C. Molnar · 2019
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Conservative Q-improvement: Reinforcement learning for an interpretable decision-tree policy
A. M. Roth, N. Topin, P. Jamshidi, and M. Veloso · 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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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
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E. Angelino, N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin · 2017
Cited alongside, same era.
Interpretability via model extraction
O. Bastani, C. Kim, and H. Bastani · 2017
Cited alongside, same era.
Interpreting blackbox models via model extraction
O. Bastani, C. Kim, and H. Bastani · 2017
Cited alongside, same era.
Optimal classification trees
D. Bertsimas and J. Dunn · 2017
Cited alongside, same era.
Distilling a neural network into a soft decision tree
N. Frosst and G. E. Hinton · 2017
Cited alongside, same era.
Interpretable & explorable approximations of black box models
H. Lakkaraju, E. Kamar, R. Caruana, and J. Leskovec · 2017
Cited alongside, same era.
Artificial Intelligence - Foundations of Computational Agents
D. Poole and A. K. Mackworth · 2017
Cited alongside, same era.
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K. Müller, editors · 2019
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Desiderata for interpretability: Explaining decision tree predictions with counterfactuals
K. Sokol and P. A. Flach · 2019
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Learning optimal decision trees using constraint programming
H. Verhaeghe, S. Nijssen, G. Pesant, C. Quimper, and P. Schaus · 2019
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Learning optimal classification trees using a binary linear program formulation
S. Verwer and Y. Zhang · 2019
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Optimizing for interpretability in deep neural networks with tree regularization
M. Wu, S. Parbhoo, M. C. Hughes, V. Roth, and F. Doshi-Velez · 2019
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Explainable AI: A brief survey on history, research areas, approaches and challenges
F. Xu, H. Uszkoreit, Y. Du, W. Fan, D. Zhao, and J. Zhu · 2019
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Learning optimal decision trees using caching branch-and-bound search
G. Aglin, S. Nijssen, and P. Schaus · 2020
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PyDL8.5: a library for learning optimal decision trees
G. Aglin, S. Nijssen, and P. Schaus · 2020
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Efficient inference of optimal decision trees
F. Avellaneda · 2020
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On symbolically encoding the behavior of random forests
A. Choi, A. Shih, A. Goyanka, and A. Darwiche · 2020
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Learning optimal decision trees with MaxSAT and its integration in adaboost
H. Hu, M. Siala, E. Hebrard, and M. Huguet · 2020
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Interpretable AI
IAI · 2020
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https://www-lrn.cs.umass.edu/iti/ , 2020
Incremental Decision Tree Induction · 2020
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SAT-based encodings for optimal decision trees with explicit paths
M. Janota and A. Morgado · 2020
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From local explanations to global understanding with explainable AI for trees
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee · 2020
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https://www.openml.org/ , 2020
OpenML: Machine learning, better, together · 2020
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https://github.com/EpistasisLab/penn-ml-benchmarks , 2020
Penn Machine Learning Benchmarks · 2020
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Optimization methods for interpretable differentiable decision trees applied to reinforcement learning
A. Silva, M. C. Gombolay, T. W. Killian, I. D. J. Jimenez, and S. Son · 2020
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https://archive.ics.uci.edu/ml , 2020
UCI Machine Learning Repository · 2020
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Learning optimal decision trees using constraint programming
H. Verhaeghe, S. Nijssen, G. Pesant, C. Quimper, and P. Schaus · 2020
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Regional tree regularization for interpretability in deep neural networks
M. Wu, S. Parbhoo, M. C. Hughes, R. Kindle, L. A. Celi, M. Zazzi, V. Roth, and F. Doshi-Velez · 2020
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