Bulk loading hierarchical mixture models for efficient stream classification

Authors:
Philipp Kranen;Ralph Krieger;Stefan Denker;Thomas Seidl
Affiliations:
Data Manangement and Exploration Department, RWTH Aachen University, Germany;Data Manangement and Exploration Department, RWTH Aachen University, Germany;Data Manangement and Exploration Department, RWTH Aachen University, Germany;Data Manangement and Exploration Department, RWTH Aachen University, Germany
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2010

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Abstract

The ever growing presence of data streams led to a large number of proposed algorithms for stream data analysis and especially stream classification over the last years Anytime algorithms can deliver a result after any point in time and are therefore the natural choice for data streams with varying time allowances between two items Recently it has been shown that anytime classifiers outperform traditional approaches also on constant streams Therefore, increasing the anytime classification accuracy yields better performance on both varying and constant data streams In this paper we propose three novel approaches that improve anytime Bayesian classification by bulk loading hierarchical mixture models In experimental evaluation against four existing techniques we show that our best approach outperforms all competitors and yields significant improvement over previous results in term of anytime classification accuracy.