Concept Drift Data Stream Classification Algorithm Based on Incremental Weighting

Concept drift is one of the most common phenomena in data stream mining, where the underlying knowledge patterns in the data stream change dynamically over time, leading to a decline in the accuracy of previously established classifiers. To address this issue, we propose a concept drift data stream classification algorithm based on incremental weighting abbreviated as SCIW algorihtm. This algorithm employs a heuristic weight updating strategy combined with an adaptive method based on accuracy differences, and improves the Poisson distribution-based resampling strategy. The SCIW is capable of adapting to various concept drifts, effectively mitigating the decline in classifier accuracy. Experimental results on 14 synthetic datasets and 6 real-world datasets demonstrate that SCIW and adaptive random forests (ARF) outperform other algorithms in terms of accuracy. Additionally, SCIW significantly excels ARF in terms of time and memory consumption, with the overall average time consumption being approximately 83% of that of ARF and the overall average memory consumption being approximately 13% of that of ARF algorithm.