Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing
Abstract
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. A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR), is implemented under the HHT framework thereafter. By substituting a widely-acknowledged retraining scheme with an adaptive training strategy, we further demonstrate that the concept drift adaptation capability of HLFR can be significantly boosted. The theoretical analysis on the Type-I and Type-II errors of HLFR is also performed. Experiments on both simulated and real-world datasets illustrate that our methods outperform state-of-the-art methods in terms of detection precision, detection delay as well as the adaptability across different concept drift types.
Cite
@article{arxiv.1707.07821,
title = {Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing},
author = {Shujian Yu and Zubin Abraham and Heng Wang and Mohak Shah and Yantao Wei and José C. Príncipe},
journal= {arXiv preprint arXiv:1707.07821},
year = {2019}
}
Comments
Manuscript accepted by the Journal of The Franklin Institute. A short version of this manuscript, titled "Concept Drift Detection with Hierarchical Hypothesis Test", was presented at the 2017 SIAM International Conference on Data Mining (SDM) https://epubs.siam.org/doi/10.1137/1.9781611974973.86