Incremental Semi-Supervised classification of data streams via self-representative selection

• We advance an Incremental Semi-Supervised classification (ISSC) approach via Self-Representative Selection (IS3RS).
• We develop an incremental self-representative data selection strategy.
• Most representative exemplars from the sequential data chunk are incrementally labeled to expand the training set.

Incremental learning has been developed for supervised classification, where knowledge is accumulated incrementally and represented in the learning process. However, labeling sufficient samples in each data chunk is of high cost, and incremental technologies are seldom discussed in the semi-supervised paradigm. In this paper we advance an Incremental Semi-Supervised classification approach via Self-Representative Selection (IS3RS) for data streams classification, by exploring both the labeled and unlabeled dynamic samples. An incremental self-representative data selection strategy is proposed to find the most representative exemplars from the sequential data chunk. These exemplars are incrementally labeled to expand the training set, and accumulate knowledge over time to benefit future prediction. Extensive experimental evaluations on some benchmarks have demonstrated the effectiveness of the proposed framework.

An illustration of the proposed IS3RS approach.Figure optionsDownload as PowerPoint slide