Keynote - Overcoming the Challenge of Limited Labeled Data in Online Data Stream Learning

Leandro Mnku, University of Birmingham, UK; ABSTRACT: The volume and incoming speed of data have increased tremendously over the past years. Data frequently arrive continuously over time in the form of streams, rather than forming a single static data set. Therefore, data stream learning, which is able to learn incoming data upon arrival, is an increasingly important approach to extract knowledge from data. Data stream learning is a challenging task, because the underlying probability distribution of the problem is typically not static; instead, it suffers changes over time. Such challenge is exacerbated by the fact that, even though the rate of incoming examples may be very large, only a small portion of these examples may arrive as labeled examples for training, due to the high cost of the labelling process. In this talk, I will present a novel online semi-supervised data stream neural network to cope with these issues. I will also discuss further research directions to tackle these and other challenges posed by real world data stream learning applications.