We propose a modified particle filter based learning method and apply it to learning logistic regression models from evolving data streams. The resampling step of the particle filter is replaced by choosing a set of regression coefficients (i.e., particles) that maximizes the training accuracy. The method inherently handles concept drifts in the data stream and is able to learn an ensemble of logistic regression models with particle filtering. We evaluate the method on both synthetic and real data sets and find that our method outperforms other state-of-the-art algorithms on the data sets tested.

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