Abstract

The paper deals with incremental concept learning from classified examples. In many real-world applications, the target concepts of interest may change over time, and incremental learners should be able to track such changes and adapt to them. The problem is known in the literature as concept drift. The paper presents a new method for learning in such changing environments. In particular, it addresses the problem of learning drifting concepts from noisy data. We present an algorithm that is both robust against noise and quick at recognizing and adapting to changes in the target concepts. The method has been implemented in a system named FLORA4, the latest member of a whole family of learning algorithms. Experiments demonstrate significant improvement over previous results, both in noise-free and noisy situations. 1 Introduction In many real-world domains, the context on which some concepts of interest depend may change, resulting in more or less abrupt and radical changes in the defin...

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