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As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules.
On the learnability of Boolean formulae
Kearns, M., Li, M., Pitt, L., and Valiant, L · 1987
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Simplifying decision trees
Quinlan, J. R · 1987
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Learning decision lists
Rivest, R. L · 1987
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The CN2 induction algorithm
Clark, P. and Niblett, T · 1989
Earlier work this paper cites.
A nearest hyperrectangle learning method
Salzberg, S · 1991
Earlier work this paper cites.
Fast effective rule induction
Cohen, W. W · 1995
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Extracting tree-structured representations of trained networks
Craven, M. W. and Shavlik, J. W · 1996
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Unifying instance-based and rule-based induction
Domingos, P · 1996
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Integrating classification and association rule mining
Liu, B., Hsu, W., and Ma, Y · 1998
Cited alongside, same era.
Separate-and-conquer rule learning
Fürnkranz, J · 1999
Cited alongside, same era.
Minimization of Boolean complexity in human concept learning
Feldman, J · 2000
Cited alongside, same era.
Using neural network rule extraction and decision tables for credit-risk evaluation
Baesens, B., Setiono, R., Mues, C., and Vanthienen, J · 2003
Cited alongside, same era.
Parkinson’s disease: speech and voice disorders and their treatment with the lee silverman voice treatment
Ramig, L. O., Fox, C., and Sapir, S · 2004
Cited alongside, same era.
From local patterns to global models: The LeGo approach to data mining
Knobbe, A., Crémilleux, B., Fürnkranz, J., and Scholz, M · 2008
Cited alongside, same era.
Building interpretable classifiers with rules using Bayesian analysis
Letham, B., Rudin, C., McCormick, T. H., and Madigan, D · 2012
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Making machine learning models interpretable
Vellido, A., Martín-Guerrero, J. D., and Lisboa, P. J.G · 2012
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UCI machine learning repository
Lichman, M · 2013
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Exact rule learning via Boolean compressed sensing
Malioutov, D. M. and Varshney, K. R · 2013
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Comprehensible classification models – a position paper
Freitas, A. A · 2014
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Learning optimized Or’s of And’s
Wang, T. and Rudin, C · 2015
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Domain knowledge integration in data mining using decision tables: case studies in churn prediction
Lima, E., Mues, C., and Baesens, B · 2009
Cited alongside, same era.
Foundations of rule learning
Fürnkranz, J., Gamberger, D., and Lavrač, N · 2012
Cited alongside, same era.
http://www-03.ibm.com/software/products/en/ibmilogcpleoptistud
IBM ILOG CPLEX optimization studio
Cited in the paper.
Bayesian Or’s of And’s for interpretable classification with application to context aware recommender systems
Wang, T., Rudin, C., Doshi-Velez, F., Liu, Y., Klampfl, E., and MacNeille, P · 2015
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Engineering safety in machine learning
Varshney, K. R · 2016
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