Learning decision lists

By R. Rivest. 2004

515 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical Learning Theory

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Citations in the corpus

Fairwashing: the risk of rationalization (2019, background): “A rule list (Rivest, 1987; Letham et al., 2015; Angelino et al., 2018) d = (dp, δp, q0,K) of length K ≥ 0 is a (K + 1)−tuple consisting of K distinct association rules rk = pk → qk, in which pk ∈ dp is the antecedent of the association rule and qk ∈ δp its corresponding consequent, followed by a def”
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning (2016, background): “There have been deliberate efforts to develop models that are interpretable in the recent past [4], [2], [5] .”
Interpretable Two-level Boolean Rule Learning for Classification (2015, background): “The second motivation for this formulation is to avoid identical clauses by training each clause with a diﬀerent subset of samples, as done in [10, 11, 15].”
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, methodology): “In the past, associative classiﬁers have been constructed from heuristic greedy sorting mechanisms [Rivest (1987), Liu, Hsu and Ma (1998), Marchand and Sokolova (2005), Rudin, Letham and Madigan (2013)].”
Learning Certifiably Optimal Rule Lists (2017, methodology): “Methods for decision list learning construct rule lists iteratively in a greedy way (Rivest, 1987; Sokolova et al., 2003; Marchand and Sokolova, 2005; Rudin et al., 2013; Goessling and Kang, 2015); these too have no guarantee of optimality, and tend not to produce optimal rule lists in general.”
Optimal Decision Lists using SAT (2020, methodology): “Decision lists were introduced by Rivest (1987) and heuristic methods for decision lists also date back to the late 80s (Clark and Niblett 1989; Clark and Boswell 1991).”
Learning Fair Rule Lists (2019, background): “As shown in [55], rule lists generalize decision trees.”
MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules (2018, background): “The most popular interpretable techniques that tend to be adopted and trusted by decision makers include classification rules, decision trees, and decision lists [10,29,8,30].”
Boolean Decision Rules via Column Generation (2018, background): “Rule sets stand in contrast to decision lists [44, 35, 49, 3, 34, 53], where rules are ordered in an IF-ELSE sequence, and decision trees [11, 43, 6], where they are organized into a tree structure.”
An optimization approach to learning falling rule lists (2017, neutral): “0 (Quinlan, 2004), and decision list learning (Rivest, 1987).”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, methodology): “The resulting ordered rules are instances of decision lists [34].”
Scalable Bayesian Rule Lists (2016, neutral): “Inductive logic programming (Muggleton & De Raedt, 1994), greedy top-down decision list algorithms (Rivest, 1987; Sokolova, Marchand, Japkowicz, & Shawe-Taylor, 2003; Anthony, 2005; Marchand & Sokolova, 2005; Rudin, Letham, & Madigan, 2013; Goessling & Kang, 2015), associative classification (Vanhoo”
Falling Rule Lists (2014, methodology): “The vast majority of other methods that discretize the input space are also greedy, such as decision tree (Quinlan, 1986, 1993) and decision list methods (Rivest, 1987; Fawcett, 2008).”
Learning Explainable Decision Rules via Maximum Satisfiability (2020, background): “Other works consider learning decision lists and decision sets, which are generalizations of k-cnf rules [15], [16].”
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, methodology): “…we use the concept of decision list, which was developed in the computer science literature for representing flexible but interpretable classifiers (Rivest, 1987; Clark and Niblett, 1989; Marchand and Sokolova, 2005; Letham et al., 2012; Wang and Rudin, 2014); see Freitas (2014) for a recent…”
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, background): “There exist a range of technical approaches to derive each form of explanation, whether it be learning sparse models [Mehmood et al., 2012, Chandrashekar and Sahin, 2014], monotone functions [Canini et al., 2016], or efﬁcient logic-based models [Rivest, 1987].”