Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling
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Mining data streams poses a number of challenges, including the continuous and non-stationary nature of data, the massive volume of information to be processed and constraints put on the computational resources. While there is a number of supervised solutions proposed for this problem in the literature, most of them assume that access to the ground truth (in form of class labels) is unlimited and such information can be instantly utilized when updating the learning system. This is far from being realistic, as one must consider the underlying cost of acquiring labels. Therefore, solutions that can reduce the requirements for ground truth in streaming scenarios are required. In this paper, we propose a novel framework for mining drifting data streams on a budget, by combining information coming from active learning and self-labeling. We introduce several strategies that can take advantage of both intelligent instance selection and semi-supervised procedures, while taking into account the potential presence of concept drift. Such a hybrid approach allows for efficient exploration and exploitation of streaming data structures within realistic labeling budgets. Since our framework works as a wrapper, it may be applied with different learning algorithms. Experimental study, carried out on a diverse set of real-world data streams with various types of concept drift, proves the usefulness of the proposed strategies when dealing with highly limited access to class labels. The presented hybrid approach is especially feasible when one cannot increase a budget for labeling or replace an inefficient classifier. We deliver a set of recommendations regarding areas of applicability for our strategies.
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