Unsupervised Drift Detector Ensembles for Data Stream Mining Data stream mining is among the most contemporary branches of machine learning. The potentially infinite sources give us many opportunities and at the same time pose new challenges. To properly handle streaming data we need to improve our wellestablished methods, so they can work with dynamic data and under strict constraints. Supervised streaming machine learning algorithms require a certain number of labeled instances in order to stay uptodate. Since high budgets dedicated for this purpose are usually infeasible, we have to limit the supervision as much as we can. One possible approach is to trigger labeling, only if a change is explicitly indicated by a detector. While there are several supervised algorithms dedicated for this purpose, the more practical unsupervised ones are still lacking a proper attention. In this paper, we propose a novel unsupervised ensemble drift detector that recognizes local changes in feature subspaces (EDFS) without additional supervision, using specialized committees of incremental KolmogorovSmirnov tests. We combine it with an adaptive classifier and update it, only if the drift detector signalizes a change. Conducted experiments show that our framework is able to efficiently adapt to various concept drifts and outperform other unsupervised algorithms.

Streaming Random Patches for Evolving Data Stream Classification Ensemble methods are a popular choice for learning from evolving data streams. This popularity is due to (i) the ability to simulate simple, yet, successful ensemble learning strategies, such as bagging and random forests; (ii) the possibility of incorporating drift detection and recovery in conjunction to the ensemble algorithm; (iii) the availability of efficient incremental base learners, such as Hoeffding Trees. In this work, we introduce the Streaming Random Patches (SRP) algorithm, an ensemble method specially adapted to stream classification which combines random subspaces and online bagging. We provide theoretical insights and empirical results illustrating different aspects of SRP. In particular, we explain how the widely adopted incremental Hoeffding trees are not, in fact, unstable learners, unlike their batch counterparts, and how this fact significantly influences ensemble methods design and performance. We compare SRP against stateoftheart ensemble variants for streaming data in a multitude of datasets. The results show how SRP produce a high predictive performance for both real and synthetic datasets. Besides, we analyze the diversity over time and the average tree depth, which provides insights on the differences between local subspace randomization (as in random forest) and global subspace randomization (as in random subspaces).