NDLI: Detecting and adapting to drifting concepts

Content Provider	IEEE Xplore Digital Library
Author	Haixia Chen Shengxian Ma Kai Jiang
Copyright Year	2012
Description	Author affiliation: The 45th Research Institute of CETC, Beijing, China (Kai Jiang) \|\| Science and Technology on Electro-optical Information, Security Control Laboratory, Beijing, China (Haixia Chen; Shengxian Ma)
Abstract	The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method employs hypothesis test as mechanism for detecting concept drift and learns a base classifier for each new training data chunk. Former classifiers deemed as usable by the hypothesis test mechanism and the new classifiers are integrated to form the final classifiers ensemble for prediction. The main focus of the work is to identify the usability of base classifiers that representing the same or similar concept with the current one, make full use of the older valid information together with the newer examples to improve classification accuracy, and avoid the interference of classifiers representing conflictive concepts with the current one. Experiments with simulated concept drift scenarios compared the proposed method with other approaches. The results showed that the method could consistently recognize different types of drift, adapt quickly to these changes to maintain its performance level, and utilize the former knowledge to improve its performance for recurring context.
Starting Page	775
Ending Page	779
File Size	272578
Page Count	5
File Format	PDF
ISBN	9781467300254
e-ISBN	9781467300247
DOI	10.1109/FSKD.2012.6234061
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Learning systems Accuracy Error analysis concept drift classifier ensemble Machine learning Classification algorithms Data mining incremental learing
Content Type	Text
Resource Type	Article

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