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. A new level transition threshold parameter is proposed, and a piecewise weighting mechanism including "Stable Level-Warning Level-Drift Level" is innovatively introduced in concept drift detection. The instances in the window are weighted in levels, and it is applied to the double sliding window. Based on this, a multi-level weighted drift detection method(MWDDM) is proposed. Especially, two variants which are MWDDM_H and MWDDM_M are proposed basd on Hoeffding inequality and Mcdiarmid inequality respectively. Experiments on artificial datasets show that MWDDM can detect abrupt and gradual concept drift faster than any other comparison algorithms, while maintaining a low false positive ratio and false alarm rate. Experiments on real-world datasets show that MWDDM has the highest classification accuracy in most cases.

Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing

“…The research results in this paper will help to improve the efficiency and accuracy of FP mining, which is of great theoretical significance and important for solving problems in practical applications [46]. For example, in recommendation systems in cloud environments, changes in user behavior can be monitored in real time using the proposed multitype CD detection method, so as to adjust the recommendation strategy and improve the accuracy and user satisfaction with the recommendation system [47]. In addition, the method can also be applied in the fields of anomaly detection, system monitoring and decision support in cloud environments [48].…”