Learning customized and optimized lists of rules with mathematical programming

We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and prunin...

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Veröffentlicht in:	Mathematical programming computation Jg. 10; H. 4; S. 659 - 702
Hauptverfasser:	Rudin, Cynthia, Ertekin, Şeyda
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018 Springer Nature B.V
Schlagworte:	Accuracy Decision trees Digital Object Identifier Full Length Paper Machine learning Mathematical analysis Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Operations Research/Decision Theory Optimization Pruning Sparsity Theory of Computation 68T05 Learning and adaptive systems Learning and adaptive systems Sparsity 90C11 Mixed integer programming Mixed-integer programming 62-04 Explicit machine computation and programs (not the theory of computation or programming) Decision lists Interpretable modeling Decision trees Artificial intelligence Associative classification 68T05—Computer Science
ISSN:	1867-2949, 1867-2957
Online-Zugang:	Volltext
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