λ ‐ Perceptron : An adaptive classifier for data streams

Streaming data introduce challenges mainly due to changing data distributions (population drift). To accommodate population drift we develop a novel linear adaptive online classification method motivated by ideas from adaptive filtering. Our approach allows the impact of past data on parameter estim...

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Bibliographic Details
Published in:Pattern recognition 2011, Vol.44 (1), p.78-96
Main Authors: Pavlidis, N.G., Tasoulis, D.K., Adams, N.M., Hand, D.J.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Classification
Computer science
control theory
systems
Connectionism. Neural networks
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Forgetting
Information, signal and communications theory
Online learning
Population drift
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Streaming data
Telecommunications and information theory
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Summary:Streaming data introduce challenges mainly due to changing data distributions (population drift). To accommodate population drift we develop a novel linear adaptive online classification method motivated by ideas from adaptive filtering. Our approach allows the impact of past data on parameter estimates to be gradually removed, a process termed forgetting, yielding completely online adaptive algorithms. Extensive experimental results show that this approach adjusts the forgetting mechanism to maintain performance. Moreover, it might be possible to exploit the information in the evolution of the forgetting mechanism to obtain information about the type and speed of the underlying population drift process.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2010.07.026