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We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs).
Incremental induction of decision trees
Paul E. Utgoff · 1989
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Modeling of signal-response cascades using decision tree analysis
Sampsa Hautaniemi, Sourabh Kharait, Akihiro Iwabu, Alan Wells, and Douglas A. Lauffenburger · 2005
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Translating pseudo-boolean constraints into SAT
Niklas Eén and Niklas Sörensson · 2006
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Luc De Raedt, Tias Guns, and Siegfried Nijssen · 2008
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Minimising decision tree size as combinatorial optimisation
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Handbook of Satisfiability , volume 185 of Frontiers in Artificial Intelligence and Applications , 2009. IOS Press
Armin Biere, Marijn Heule, Hans van Maaren, and Toby Walsh, editors · 2009
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The sat4j library, release 2.2
Daniel Le Berre and Anne Parrain · 2010
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The sat4j library, release 2.2
Daniel Le Berre and Anne Parrain · 2010
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A max-sat-based approach to constructing optimal covering arrays
Carlos Ansótegui, Idelfonso Izquierdo, Felip Manyà, and José Torres Jiménez · 2013
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Pmlb: a large benchmark suite for machine learning evaluation and comparison
Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore · 2017
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Learning optimal decision trees with SAT
Nina Narodytska, Alexey Ignatiev, Filipe Pereira, and João Marques-Silva · 2018
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Learning optimal classification trees using a binary linear program formulation
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Xiyang Hu, Cynthia Rudin, and Margo I. Seltzer · 2019
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Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, and Pierre Schaus · 2019
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Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS, April 2021
European Commission · 2021
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