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Lambda-perceptron: an adaptive classifier for data-streams

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<mark>Journal publication date</mark>01/2011
<mark>Journal</mark>Pattern Recognition
Issue number1
Number of pages19
Pages (from-to)78-96
Publication StatusPublished
<mark>Original language</mark>English


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.