Learning Decision Lists

By R. Rivest. 1987

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Computing Optimal Decision Sets with SAT (2020, methodology): “a given set of training samples, which is then tackled by the interior point approach. Later, [34] considered decision sets as a more explainable alternative to decision trees [10] and decision lists [50]. The method of [34] yields a set of rules and heuristically minimizes a linear combination of c”
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019, background): “The study of designing interpretable machine learning clas-siﬁers can ﬁnd its root in the development of popular models such as decision trees (Bessiere, Hebrard, and OSullivan 2009; Quinlan 2014), decision lists (Rivest 1987), classiﬁ-cation rules (Cohen 1995) etc.”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “, [4,33,39,40,42,55]), inductive logic programming approaches (e.”
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013, methodology): “The decision list is learned from a large dataset with many features, which provides better accuracy than the few handselected features used in the CHADS 2 score, yet the same level of interpretability.”
A statistical approach to rule learning (2006, background): “• k-decision lists: decision lists with at most k literals per term (Rivest, 1987)”
Word sense disambiguation: A survey (2009, background): “Decision Lists A decision list [Rivest 1987] is an ordered set of rules for categorizing test instances (in the case of WSD, for assigning the appropriate sense to a target word).”
Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, methodology): “Many of the older methods in the artificial intelligence literature, including decision lists and decision trees are interpretable [12, 14, 38, 39].”
Machine Learning for Information Extraction in Informal Domains (2000, background): “Rivest introduces the notion of decision lists and discusses the class k-DL of decision lists having conjunctive clauses of size k at each decision (Rivest, 1987).”
Interpretable Dynamic Treatment Regimes (2016, background): “hich is represented as a sequence if-then statements mapping logical clauses to treatment recommendations. Decision rules of this form are a special case of tree-based rules, known as decision lists (Rivest, 1987; Marchand and Sokolova, 2005; Letham et al., 2012; Wang and Rudin, 2015; Zhang et al., ”
A SAT-Based Approach to Learn Explainable Decision Sets (2018, background): “Rule lists impose an order of the rules [35], whereas decision sets do not.”
The relationship between trust in AI and trustworthy machine learning technologies (2019, background): “• Explainability Technologies: technologies focused on explaining and interpreting the outcome to the stakeholders (including end-users) in a humane manner [6, 60, 17, 41, 86, 33, 19, 42, 76, 62, 89, 69].”