An Adaptive Learning Approach for Noisy Data Streams

Authors:
Fang Chu;Yizhou Wang;Carlo Zaniolo
Affiliations:
University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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Abstract

Two critical challenges typically associated with mining data streams are concept drift and data contamination. To address these challenges, we seek learning techniques and models that are robust to noise and can adapt to changes in timely fashion. We approach the stream-mining problem using a statistical estimation framework, and propose a fast and robust discriminative model for learning noisy data streams. We build an ensemble of classifiers to achieve timely adaptation by weighting classifiers in a way that maximizes the likelihood of the data. We further employ robust statistical techniques to alleviate the problem of noise sensitivity. Experimental results on both synthetic and real-life data sets demonstrate the effectiveness of this new model learning approach.