Incremental Learning for Interactive E-Mail Filtering

Incremental Learning for Interactive E-Mail Filtering

Ding-Yi Chen (University of Queensland, Australia), Xue Li (University of Queensland, Australia), Zhao Yang Dong (University of Queensland, Australia) and Xia Chen (University of Queensland, Australia)
DOI: 10.4018/jitwe.2006040104
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In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

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