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Stream mining poses unique challenges to machine learning: predictive modelsare required to be scalable, incrementally trainable, must remain bounded insize (even when the data stream is arbitrarily long), and be nonparametric inorder to achieve high accuracy even in complex and dynamic environments.Moreover, the learning system must be parameterless ---traditional tuningmethods are problematic in streaming settings--- and avoid requiring priorknowledge of the number of distinct class labels occurring in the stream. Inthis paper, we introduce a new algorithmic approach for nonparametric learningin data streams. Our approach addresses all above mentioned challenges bylearning a model that covers the input space using simple local classifiers.The distribution of these classifiers dynamically adapts to the local (unknown)complexity of the classification problem, thus achieving a good balance betweenmodel complexity and predictive accuracy. We design four variants of ourapproach of increasing adaptivity. By means of an extensive empiricalevaluation against standard nonparametric baselines, we show state-of-the-artresults in terms of accuracy versus model size. For the variant that imposes astrict bound on the model size, we show better performance against all othermethods measured at the same model size value. Our empirical analysis iscomplemented by a theoretical performance guarantee which does not rely on anystochastic assumption on the source generating the stream.

The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams