Adaptive Incremental-Learning Ensemble Classification Approach for Concept Drift Problem
HAN Mingming, SUN Guanglu, ZHU Suxia · DOAJ (DOAJ: Directory of Open Access Journals) · 2020
The performance of the machine learning model always decreases with the occurrence of concept drift due to the non-stationary characteristics of the data flow. This paper studies how the classifier adapts to concept drift, and proposes an incremental learning ensemble algorithm with small data blocks as input to deal with data stream classification under concept drift. This algorithm does not have complex parameters, but it puts forward higher requirements for weak classifiers. After removing unqualified weak classifiers, new weak classifiers are added. During the incremental training, weights of samples and weak classifiers are updated according to training error. Finally, the prediction results of each weak classifier are integrated by weighted voting. In this paper, five artificial datasets with specific drift and three real datasets with unknown drift are used for experiments, and compared with four existing algorithms, the experimental results show that the algorithm can deal with the data flow classification problem under concept drift.