Sequential Event Prediction with Association Rules

By Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, E. Kogan, David Madigan. 2011

57 citations recorded by the source corpus.

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Citations in the corpus

Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “This work follows a line of work on optimal rule lists, motivated by Bayesian approaches [37,57,58,70,73], with earlier closely related ideas in the PhD thesis of Allison Chang [12], with direct follow-up work to this paper in Angelino et al.”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, background): “HARM’s estimates of conditional probability are based on the principle of the adjusted confidence [Rudin et al. 2011], where rules that do not appear often enough in the training-set may not considered to be trustworthy enough to make accurate predictions.”
Deep Sequential Context Networks for Action Prediction (2017, background): “The prediction of future events was also investigated in other applications, such as predicting events in recommendation systems [16, 23], predicting future visual representation [29], and reasoning about the preferred path for a person [11, 1].”
Sequence-Aware Recommender Systems (2018, methodology): “Alternatively, different algorithm variants were proposed that use multiple minimum support values or “adjusted” confidence scores [95].”
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013, unknown): “Call the rule˜r i .”
Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, background): “HARM’s estimates of conditional probability are based on the principle of the adjusted confidence [37], where rules that do not appear often enough in the database may not considered to be trustworthy enough to make accurate predictions.”
Learning theory analysis for association rules and sequential event prediction (2013, result): “This result strengthens the one in the conference version of this work (Rudin et al., 2011), where we used the bound for pointwise hypothesis stability; uniform stability implies pointwise hypothesis stability, so the result in the conference version follows automatically.”
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].”