NDLI: Dynamic classifier selection for effective mining from noisy data streams

Content Provider	IEEE Xplore Digital Library
Author	Xingquan Zhu Xindong Wu Ying Yang
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Vermont Univ., Burlington, VT, USA (Xingquan Zhu; Xindong Wu; Ying Yang)
Abstract	Mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single "best" classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	305
Ending Page	312
File Size	300022
Page Count	8
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10091
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Testing Distributed control Computer science Application software Credit cards Protection Heuristic algorithms Partitioning algorithms
Content Type	Text
Resource Type	Article

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