A multiple-instance stream learning framework for adaptive document categorization

The task of document categorization is to classify documents from a stream as relevant or non-relevant to a particular user interest so as to reduce information overload. Existing solutions typically perform classification at the document level, i.e., a document is returned as relevant if at least a part of the document is of interest of the user. In this paper, we propose a novel multiple-instance stream learning framework for adaptive document categorization, named MIS-DC. Our proposed approach has the ability of making accuracy prediction at both the document level and the block level, while only requires labeling the training documents at the document level. In addition, our proposed approach can also provide adaptive document categorization by detecting and handling concept drift at a finer granularity when data streams evolve over time, thereby yielding higher prediction accuracy than existing data stream algorithms. Experiments on benchmark and real-world datasets have demonstrated the effectiveness of our proposed approach.