A novel drift detection method using parallel detection and anti-noise techniques

With the rapid development of the Internet industry, a large amount of streaming data with significant application value will be generated on the Internet. The distribution of stream data is evolving over time compared to traditional data, posing a significant challenge in the learning process from streaming data. In order to adapt the change of data distribution, concept drift detection methods are proposed to pinpoint when the concept drift occurs. Most existing drift detection methods, however, overlook the improvement of the current classifier and the influence of noise data on drift detection. This oversight leads to a decrease in the effectiveness of drift detection. In this paper, we propose a novel adaptation drift detection method to overcome the shortcomings of previous algorithms, such as error detection and lack of anti-noise capability. Meanwhile, stream computing and parallel computing are used to enhance the efficiency of our algorithm. The results of a simulation experiment on 9 synthetic stream data and 6 real-world stream data, all exhibiting concept drift, demonstrate that our method is more effective in handling concept drift compared to other state-of-the-art methods.