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Regular data classification techniques are based mainly on two strong assumptions: (1) the existence of a reasonably large labeled set of data to be used in training; and (2) future input data instances conform to the distribution of the training set, i.e. data distribution is stationary along time. However, in the case of data stream classification, both of the aforementioned assumptions are difficult to satisfy. In this paper, we present a graph-based semi-supervised approach that extends the static classifier based on the K-associated Optimal Graph to perform online semi-supervised classification tasks. In order to learn from labeled and unlabeled patterns, here we adapt the optimal graph construction to simultaneously spread the labels in the training set. The sparse, disconnected nature of the proposed graph structure gives flexibility to cope with non-stationary classification. Experimental comparison between the proposed method and three state-of-the-art ensemble classification methods is provided and promising results have been obtained.

Partially labeled data stream classification with the semi-supervised K-associated graph