Multi-label Stream Classification with Self-Organizing Maps
This addresses the challenge of adapting to concept drift in multi-label stream classification for applications like traffic monitoring and social networks, though it is incremental as it builds on existing self-organizing map and k-nearest neighbors strategies.
The paper tackles the problem of multi-label classification in data streams with concept drift and infinitely delayed labels, proposing an online unsupervised method based on self-organizing maps that achieves competitive performance with existing methods in both stationary and non-stationary scenarios.
Several learning algorithms have been proposed for offline multi-label classification. However, applications in areas such as traffic monitoring, social networks, and sensors produce data continuously, the so called data streams, posing challenges to batch multi-label learning. With the lack of stationarity in the distribution of data streams, new algorithms are needed to online adapt to such changes (concept drift). Also, in realistic applications, changes occur in scenarios of infinitely delayed labels, where the true classes of the arrival instances are never available. We propose an online unsupervised incremental method based on self-organizing maps for multi-label stream classification with infinitely delayed labels. In the classification phase, we use a k-nearest neighbors strategy to compute the winning neurons in the maps, adapting to concept drift by online adjusting neuron weight vectors and dataset label cardinality. We predict labels for each instance using the Bayes rule and the outputs of each neuron, adapting the probabilities and conditional probabilities of the classes in the stream. Experiments using synthetic and real datasets show that our method is highly competitive with several ones from the literature, in both stationary and concept drift scenarios.