SACCOS: A Semi-Supervised Framework for Emerging Class Detection and Concept Drift Adaption Over Data Streams

In this paper, we address the challenges of detecting instances from emerging classes over a non-stationary data stream during classification. In particular, instances from an entirely unknown class may appear in a data stream over time. Existing classification techniques utilize unsupervised clustering to identify the emergence of such data instances. Unfortunately, they make strong assumptions which are typically invalid in practice; (i) Most instances associated with a class are closer to each other in feature space than instances associated with different classes, (ii) Covariates of data are normalized through an oracle to overcome the effect of a few data instances having large feature values, and (iii) Labels of instances from emerging classes are readily available soon after detection. To address the challenges that occur in practice when the above assumptions are weak, we propose a practical semi-supervised emerging class detection framework. Particularly, we aim to identify similar data instances within local regions in feature space by incorporating a mutual graph clustering mechanism. Our empirical evaluation of this framework on real-world datasets demonstrates its superiority of classification performance compared to existing methods while using significantly fewer labeled instances.