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We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.
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Very simple classification rules perform well on most commonly used datasets
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CPAR: Classification based on predictive association rules
X. Yin and J. Han · 2003
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Learning with decision lists of data-dependent features
M. Marchand and M. Sokolova · 2005
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A fast way to produce optimal fixed-depth decision trees
A. Farhangfar, R. Greiner, and M. Zinkevich · 2008
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H. A. Chipman, E. I. George, and R. E. McCulloch · 2010
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European Union regulations on algorithmic decision-making and a “right to explanation”
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How we analyzed the COMPAS recidivism algorithm
J. Larson, S. Mattu, L. Kirchner, and J. Angwin · 2016
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Stop, question and frisk data, 2016
New York Police Department · 2016
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Learning customized and optimized lists of rules with mathematical programming
C. Rudin and Ş. Ertekin · 2016
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Supersparse linear integer models for optimized medical scoring systems
B. Ustun and C. Rudin · 2016
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Bayesian or’s of and’s for interpretable classification with application to context aware recommender systems
T. Wang, C. Rudin, F. Doshi-Velez, Y. Liu, E. Klampfl, and P. MacNeille · 2016
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S. Nijssen and E. Fromont · 2010
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Finding a short and accurate decision rule in disjunctive normal form by exhaustive search
P. R. Rijnbeek and J. A. Kors · 2010
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To explain or to predict?
G. Shmueli · 2010
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Structure of association rule classifiers: A review
K. Vanhoof and B. Depaire · 2010
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen, and B. Baesens · 2011
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Making machine learning models interpretable
A. Vellido, J. D. Martín-Guerrero, and P. J.G. Lisboa · 2012
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Learning certifiably optimal rule lists for categorical data
E. Angelino, N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin · 2017
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Cost-sensitive and interpretable dynamic treatment regimes based on rule lists
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Learning Certifiably Optimal Rule Lists: A Case For Discrete Optimization in the 21st Century
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Optimized risk scores
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A Bayesian framework for learning rule sets for interpretable classification
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Did a bail reform algorithm contribute to this San Francisco man’s murder?, 2017
E. Westervelt · 2017
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Scalable Bayesian rule lists
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Interpretable classification models for recidivism prediction
J. Zeng, B. Ustun, and C. Rudin · 2017
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An optimization approach to learning falling rule lists
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The accuracy, fairness, and limits of predicting recidivism
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A minimax surrogate loss approach to conditional difference estimation
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Building predictive models with rule lists, 2018
V. Kaxiras and A. Saligrama · 2018
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Systems optimizations for learning certifiably optimal rule lists
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
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Hybrid decision making: When interpretable models collaborate with black-box models
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