Mining Data Streams with Labeled and Unlabeled Training Examples

In this paper, we propose a framework to build prediction models from data streams which contain both labeled and unlabeled examples. We argue that due to the increasing data collection ability but limited resources for labeling, stream data collected at hand may only have a small number of labeled examples, whereas a large portion of data remain unlabeled but can be beneficial for learning. Unleashing the full potential of the unlabeled instances for stream data mining is, however, a significant challenge, consider that even fully labeled data streams may suffer from the concept drifting, and inappropriate uses of the unlabeled samples may only make the problem even worse. To build prediction models, we first categorize the stream data into four different categories, each of which corresponds to the situation where concept drifting may or may not exist in the labeled and unlabeled data. After that, we propose a relational k-means based transfer semi-supervised SVM learning framework (RK-TS³VM), which intends to leverage labeled and unlabeled samples to build prediction models. Experimental results and comparisons on both synthetic and real-world data streams demonstrate that the proposed framework is able to help build prediction models more accurate than other simple approaches can offer.