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Sequential Event Prediction with Association Rules

Author(s)
Rudin, Cynthia; Letham, Benjamin; Salleb-Aouissi, Ansaf; Kogan, Eugene; Madigan, David
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Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/
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Abstract
We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules, and provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an "adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis.
Date issued
2011-07
URI
http://hdl.handle.net/1721.1/67635
Department
Sloan School of Management
Journal
COLT 2011 - The 24th Conference on Learning Theory
Publisher
Omnipress
Citation
Rudin, Cynthia, et al. "Sequential Event Prediction with Association Rules." 24th Annual Conference on Learning Theory (COLT 2011), Budapest, Hungary, July 9-11, 2011.
Version: Author's final manuscript

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