Learning Patterns from Imbalanced Evolving Data Streams

Learning patterns from evolving data streams is challenging due to the characteristics of such streams: being continuous, unbounded and high speed data of non-stationary nature, which must be processed on the fly, using minimal computational resources. An additional challenge is imposed by the imbalanced data streams in many real-world applications, this difficulty becomes more prominent in multi-class learning tasks. This paper investigates the multi-class imbalance problem in non-stationary streams and develops a method to exploit realtime stream data and capture the dynamic of patterns from heterogeneous streams. In particular, we seek to extend concept drift adaptation techniques into imbalanced classes’ scenarios, and accordingly, we use an adaptive learner to classify multiple streams over a sequence of titled time windows. We include examples of the falsely classified instances in the training set, then we propose using a dynamic support threshold to discover the frequent patterns in these streams. We conduct an experiment on the car parking lots environment of a typical University with three simulated streams from sensors, smart pay stations and a mobile application. The result indicates the efficiency of applying adaptive learner approaches and modifying the training set to cope with the concept drift in multi-class imbalance scenarios, it also shows the merit of using a dynamic threshold to detect the rare patterns from evolving streams.