Detecting sudden concept drift with knowledge of human behavior

Concept drift, the change over time of the statistical properties of the target variable, is a serious problem for online learning systems. To overcome this problem, we propose a method inspired by human behavior for detecting sudden concept drift. We first conducted a human behavior experiment to investigate our working hypothesis that humans can detect changes quickly when their confident classifications are rejected despite the fact that their recent classification accuracy is high. The human behavior experiments supported our working hypothesis. We then have proposed the leaky integrate-and-detect (LID) model based on our working hypothesis. Our computer experiments showed LID was able to detect sudden changes quickly and accurately in an environment that includes noise and gradual changes.