A multi-level weighted concept drift detection method

Abstract

The concept drift detection method is an online learner. Its main task is to determine the position of drifts in the data stream, so as to reset the classifier after detecting the drift to improve the learning performance, which is very important in practical applications such as user interest prediction or financial transaction fraud detection. Aiming at the inability of existing drift detection methods to balance the detection delay, false positives, false negatives, and space–time efficiency, a new level transition threshold parameter is proposed, and a multi-level weighted mechanism including "Stable Level-Warning Level-Drift Level" is innovatively introduced in the concept drift detection. The instances in the window are weighted in levels, and the double sliding window is also applied. Based on this, a multi-level weighted drift detection method (MWDDM) is proposed. In particular, two variants which are MWDDM_H and MWDDM_M are proposed based on Hoeffding inequality and Mcdiarmid inequality, respectively. Experiments on artificial datasets show that MWDDM_H and MWDDM_M can detect abrupt and gradual concept drift faster than other comparison algorithms while maintaining a low false positive ratio and false negative ratio. Experiments on real-world datasets show that MWDDM has the highest classification accuracy in most cases while maintaining good space time efficiency.