Learning theory analysis for association rules and sequential event prediction

By Cynthia Rudin, Benjamin Letham, David Madigan. 2013

50 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Statistical Learning Theory

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “…Niblett, 1989; 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; Clark and Boswell, 1991; Gaines and Compton, 1995; Cohen, 1995; Frank and Witten, 1998; Friedman and Fisher, 1999; Marchand and…”
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.”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “[58]; namely that the VC dimension of a set of rule list classifiers is the number of rules pre-mined.”
Scalable Bayesian Rule Lists (2016, methodology): “A theoretical result states that the VC (Vapnik-Chervonenkis) dimension of the set of rule lists created using pre-mined rules is exactly the size of the set of pre-mined rules (Rudin et al., 2013).”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, methodology): “The decision tree methods are representatives of the class of greedy and heuristic methods (e.g., [10,11,13,16,17,26–28, 31, 37, 38 , 51 ] ) that”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Associative classification and rule learning methods (e.g., Ma et al., 1998; Li et al., 2001; Yin and Han, 2003; Chen et al., 2006; Cheng et al., 2007; McCormick et al., 2012; Rudin et al., 2013; Dong et al., 1999; Michalski, 1969; Clark and Niblett, 1989; Frank and Witten, 1998) mine for frequent r”
The Convolutional Tsetlin Machine (2019, background): “One prominent example is frequent itemset mining for association rule learning [2], for instance applied to predicting sequential events [35, 24].”