Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of realtime, live data about true physical events from around the world. However, while such human sensor data have been exploited for retrospective largescale event detection such as hurricanes or earthquakes, there has been limited to no success in exploiting this rich resource for general physical event detection. Prior implementation approaches have suffered from the concept drift phenomenon, where realworld data exhibits continuous, unknown, and unbounded changes in its data distribution, making static machine learning models ineffective in the long term. We propose and implement an endtoend collaborative drift adaptive system that integrates corroborative and probabilistic sources to deliver realtime predictions. Furthermore, our system is adaptive to concept drift and performs automated continuous learning to maintain high performance. We demonstrate our approach in a realtime demo available online for landslide disaster detection, with extensibility to other realworld physical events such as flooding, wildfires, hurricanes, and earthquakes.

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.