Anticipative Bayesian classification for data streams with verification latency
Vera Hofer, Georg Krempl, Dominik Lang · Journal of Applied Statistics · 2024
), an approach that is capable of integrating and automatically selecting from different components. In its Bayesian classification framework, ABClass combines density estimation techniques, extended to extrapolate drift patterns over time, with unsupervised parameter tuning and unsupervised model selection. ABClass allows for multivariate density estimation and extrapolation techniques. In this work, we assume conditional independence between features given the class label for modelling feature-specific drift patterns. ABClass is generative and can also be used for explaining and visualising concept drift patterns. It is generic, making it easy to include further types of drift models, both for the class-conditional feature distribution and for the class prior distribution. The experimental evaluation on several real-world data streams shows its competitiveness compared to other state-of-the-art approaches. ABClass is in most cases ten- to hundred-times faster than its competitors, both for model fitting and for prediction.