Learning Evolving Concepts with Online Class Posterior Probability

Abstract

In this paper, we propose a new instance-aware concept drift detection and learning algorithm, called CPP, by dynamically capturing the evolving concepts via online Class Posterior Probability. Instead of comparing distributions of two adaptive windows of data or using prediction performance to infer concept drifts, we model and trace the concept drift by investigating the change of class posterior probability via ensemble of classifier chains directly. Building upon the intuitive concept drift modeling, CPP shows several attractive benefits: (a) It is capable of detecting and learning both gradual and abrupt concept drifts effectively, and thus supports accurate predictions. (b) CPP allows distinguishing real concept drifts from noisy instances or virtual concept drifts. (c) The time-changing concepts are captured and learned at the instance level, and this suggests that CPP lends itself to fast concept drift detection and learning. Empirical results show that our method allows effective and efficient concept drift detection and has good prediction performance compared to many baselines.