NDLI: Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections

Content Provider	IEEE Xplore Digital Library
Author	Tsymbal, A. Pechenizkiy, M. Cunningham, P. Puuronen, S.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Trinity Coll., Dublin (Tsymbal, A.)
Abstract	In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at the instance level. Our experiments with real-world antibiotic resistance data demonstrate that dynamic integration of classifiers built over small time intervals can be more effective than globally weighted voting which is currently the most commonly used integration approach for handling concept drift with ensembles
Starting Page	679
Ending Page	684
File Size	190587
Page Count	6
File Format	PDF
ISBN	0769525171
ISSN	10637125
DOI	10.1109/CBMS.2006.94
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Antibiotics Immune system Voting Predictive models Pathogens Computer science Educational institutions Machine learning Data mining Databases
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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