NDLI: A Shrinkage Approach for Modeling Non-stationary Relational Autocorrelation

Content Provider	IEEE Xplore Digital Library
Author	Angin, P. Neville, J.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Stat., Purdue Univ., West Lafayette, IN (Neville, J.) \|\| Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN (Angin, P.)
Abstract	Recent research has shown that collective classification in relational data often exhibit significant performance gains over conventional approaches that classify instances individually. This is primarily due to the presence of autocorrelation in relational datasets, meaning that the class labels of related entities are correlated and inferences about one instance can be used to improve inferences about linked instances. Statistical relational learning techniques exploit relational autocorrelation by modeling global autocorrelation dependencies under the assumption that the level of autocorrelation is stationary throughout the dataset. To date, there has been no work examining the appropriateness of this stationarity assumption. In this paper, we examine two real-world datasets and show that there is significant variance in the autocorrelation dependencies throughout the relational data graphs. We develop a shrinkage technique for modeling this non-stationary autocorrelation and show that it achieves significant accuracy gains over competing techniques that model either local or global autocorrelation dependencies in isolation.
Starting Page	707
Ending Page	712
File Size	648327
Page Count	6
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.147
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Autocorrelation Predictive models Testing Computer science Data mining Statistics Performance gain Topology Classification algorithms shrinkage Relational learning collective classification
Content Type	Text
Resource Type	Article

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