流计算模式下概率粗糙集三支决策的快速计算

It is a challenging topic to carry out fast computing for three-way decision in stream computing mode. Aim at the phenomenon that the increment and decrement of dynamic objects occur synchronously in the stream computing mode, this paper proposed a fast stream computing method for probabilistic rough set three-way decision. Firstly, ytdiscussed the data mode of single-object increment and decrement updating mode in stream computing. Then, proposed the reasoning of the three-way decision domains in data increment and data decrement dynamic mode respectively based on the pattern of data variation. Finally, proposed a three-way decision dynamic incremental and decremental learning algorithm based on the above theory. The comparison experiments of eight UCI datasets show that the algorithm not only outperforms the classical three-decision algorithm in time consumption, but also has strong stability for the three-way decision thresholds.