Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes This paper reexamines existing systems in streaming classification with emerging new classes (SENC) problems, where new classes that have not been used to train a classifier may emerge in a data stream. We identify that existing systems have an unspecified assumption that emerging new classes are geometrically far from known classes, or instances of known classes are densely distributed, in the feature space. Using a class separation indicator alpha, we refine the SENC problem into an alphaSENC problem, where alpha indicates a geometric distance between two classes in the feature space. We show that while most existing systems work well in highalpha SENC problems (i.e., a new class is geometrically far from a known class or instances of known classes are densely distributed), they perform poorly in lowalpha SENC problems. To solve lowalpha SENC problems effectively, we propose an approach using nearest neighbor ensembles or SENNE. We demonstrate that SENNE is able to handle both the lowalpha and highalpha SENC problems which can appear at different times in a single data stream.

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.