  As data streams become more prevalent, the necessity for online algorithms
that mine this transient and dynamic data becomes clearer. Multi-label data
stream classification is a supervised learning problem where each instance in
the data stream is classified into one or more pre-defined sets of labels. Many
methods have been proposed to tackle this problem, including but not limited to
ensemble-based methods. Some of these ensemble-based methods are specifically
designed to work with certain multi-label base classifiers; some others employ
online bagging schemes to build their ensembles. In this study, we introduce a
novel online and dynamically-weighted stacked ensemble for multi-label
classification, called GOOWE-ML, that utilizes spatial modeling to assign
optimal weights to its component classifiers. Our model can be used with any
existing incremental multi-label classification algorithm as its base
classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles
and 7 baseline models on 7 real-world datasets from diverse areas of interest.
Our experiments show that GOOWE-ML ensembles yield consistently better results
in terms of predictive performance in almost all of the datasets, with respect
to the other prominent ensemble models.
