Dealing with Concept Drift and Class Imbalance in Multi-label Stream Classification
Eleftherios Spyromitros Xioufis, Myra Spiliopoulou, Grigorios Tsoumakas and Ioannis Vlahavas
Data streams containing objects that are (or can be) associated with more than one label at the same time are ubiquitous. In spite of its important applications, classification of streaming multi-label data is largely unexplored. Existing approaches try to tackle the problem by transferring traditional single-label stream classification practices to the multi-label domain. Nevertheless, they fail to consider some of the unique properties of the problem such as within and between class imbalance and multiple concept drift. In this paper we propose a novel multi-label stream classification method, based on the k-Nearest Neighbor algorithm, which employs windows of different sizes for each label in order to deal with multi-label stream evolution, tackling at the same time the problem of class imbalance. We compare our method with one baseline and one existing method on three real world datasets and notice increased performance.