CD-BTMSE: A Concept Drift detection model based on Bidirectional Temporal Convolutional Network and Multi-Stacking Ensemble learning

Abstract

The existence of concept drift phenomenon seriously affects the quality of data, it is an urgent need to investigate accurate concept drift detection methods to improve the data quality. Concept drift detection is widely used in the application scenarios of anomaly detection, intrusion detection, fake reviews detection, etc. In recent years, although scholars have proposed many concept drift detection methods, these methods can only cope with a single type of concept drift and are unable to effectively deal with time-series data. In this paper, we propose a Concept Drift detection model based on Bidirectional Temporal convolutional network and Multi-Stacking Ensemble learning (namely CD-BTMSE). CD-BTMSE choose six suitable base learners to solve the problems of overfitting, weak generalization ability and poor robustness of existing ensemble learning-based concept drift detection models, it also innovatively utilizes the Bidirectional Temporal Convolutional Network (BiTCN) model to improve the detection accuracy of concept drift through considering the temporal characteristics of the data as well as the bidirectional semantics in the detection process; At the same time, it utilizes the multi-stacking ensemble learning model to solve the problem of low accuracy of concept drift detection caused by the relatively high generalization error rate and poor generalization ability of the existing ensemble learning-based methods. In addition, the negative log-likelihood loss function in BiTCN model is replaced by a Focal Loss to solve the problem of category inhomogeneity, and the ReLU activation function is replaced by LeakyReLU to solve the neuron ”death” and gradient problems. Extensive experiments on six public datasets show that the proposed CD-BTMSE model outperforms seven state-of-the-art concept drift detection models in terms of accuracy, precision, recall and F1-measure, and it also has better stable performance.