基于可变精度粗集模型的增量式规则获取算法

An incremental rule acquisition algorithm based on variable precision rough set model

为了获取最小决策规则集,当增加新样本时,传统的方法通常需要对决策表中所有数据重新计算,效率欠佳。从可变精度粗集模型理论出发,讨论了新增记录与已有条件属性等价类的关系及对规则集的影响,在此基础上提出了基于可变精度粗集模型的增量式规则获取算法。通过仿真实验表明,这种增量式算法是可行的。

In order to find a minimal set of rules for a decision table, the classical method cannot effectively deal with new instances added to the universe because of recalculation for the overall set of instance. In this paper, first, the relation of the new instances with the existing condition class,and effect on rule sets are studied when a new instance comes. Second, a new incremental learning algorithm based on variable precision rough set model is presented. Finally, the test results proves this algrothm feasible.