Sequential Event Prediction with Association Rules
Name
Rudin_Sequential Event.pdf
Size
1.27 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Rudin, Cynthia
Letham, Benjamin
Salleb-Aouissi, Ansaf
Kogan, Eugene
Madigan, David
Date Issued
July 2011
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
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.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.informatik.uni-trier.de/~ley/db/conf/colt/
