Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing

25 Jul 2017Shujian YuZubin AbrahamHeng WangMohak ShahYantao WeiJosé C. Príncipe

A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a phenomenon also known as concept drifts), such that their classification performance deteriorates dramatically. In this paper, we first present a hierarchical hypothesis testing (HHT) framework that can detect and also adapt to various concept drift types (e.g., recurrent or irregular, gradual or abrupt), even in the presence of imbalanced data labels... (read more)

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