Learning with Decision Lists of Data-Dependent Features

By Mario Marchand, Marina Sokolova. 2005

59 citations recorded by the source corpus.

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In Statistical Learning Theory

Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “Associative classification methods assemble decision lists or decision sets from a set of pre-mined rules, generally either by greedily adding rules to the model one by one, or simply including all “top-scoring” rules into a decision set, where each rule is scored separately according to a scoring f”
Learning Certifiably Optimal Rule Lists (2017, background): “Similarly, there are decision list and associative classification methods that construct rule lists iteratively in a greedy way (Rivest, 1987; Liu et al., 1998; Li et al., 2001; Yin and Han, 2003; Sokolova et al., 2003; Marchand and Sokolova, 2005; Vanhoof and Depaire, 2010; Rudin et al., 2013).”
Learning customized and optimized lists of rules with mathematical programming (2018, neutral): “, [4,33,39,40,42,55]), inductive logic programming approaches (e.”
Scalable Bayesian Rule Lists (2016, neutral): “…& 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 (Vanhoof & Depaire, 2010; Liu et al.,…”
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, methodology): “To obtain an interpretable and parsimonious treatment regime, 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; ”
RuleMatrix: Visualizing and Understanding Classifiers with Rules (2018, methodology): “There exist various algorithms that can construct a list of rules from training data [11,23,45,47].”
Interpretable Dynamic Treatment Regimes (2016, background): “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., 2015), that are immediately interpretable in a domain context as they can be expressed in either…”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, unknown): “In the past, associative classifiers have been built from heuristic mechanisms [Rivest 1987; Liu et al. 1998; Li et al. 2001; Yin and Han 2003; Marchand and Sokolova 2005].”
Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation (2006, background): “We will not discuss VC dimension (Vapnik 1995), average margin (Gentile & Warmuth 1999), generalization bounds (Marchand & Sokolova 2005), and so on. whereas Dietterich (1998) says that only a special version of t-test can evaluate learners.”
Random intersection trees (2013, methodology): “Examples include Rule Ensembles (Friedman and Popescu, 2008), Node Harvest (Meinshausen, 2010) and the general framework of Decision Lists (Marchand and Sokolova, 2006; Rivest, 1987).”
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers (2007, methodology): “…Shawe-Taylor (2002) and Marchand and Sokolova (2005), a SCM classifier is a conjunction of data-dependent Boolean-valued features.4 For simplicity, let us limit ourselves to the case where this set of features consists of data-dependent balls and holes; as described in Marchand and Sokolova (2005).”
Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, background): “In the past, associative classifiers have been constructed from heuristic sorting mechanisms [22, 25, 29, 36, 37, 44, 45].”
ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification (2012, background): “The second category of sorted-rule-based classification algorithms produces decision lists [see Rivest, 1987, Klivans and Servedio, 2006, Sokolova et al., 2003, Anthony, 2005, Long and Servedio, 2007, Marchand and Sokolova, 2005, Rudin et al., 2011].”
Learning theory analysis for association rules and sequential event prediction (2013, methodology): “Usually decision lists are formed the same way as decision trees are formed, which is by greedily splitting on each nodes to form the tree, and then pruning (as in for instance, Li et al., 2001; Yin and Han, 2003; Simon et al., 2011; Marchand and Sokolova, 2005).”
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013, unknown): “Call the rule˜r i .”