一种基于粗集理论的增量式学习改进算法

Improved algorithm for incremental learning based on rough sets theory

增量式学习中,当向决策表中增加一个新例子时,为了获得极小决策规则集,一般方法是对决策表中的所有数据重新计算。但这种方法显然效率很低,而且也是不必要的。论文从粗集理论出发,提出了一种最小重新计算的标准,并在此基础上,给出了一个增量式学习的改进算法。该算法在一定程度上优于传统的增量式学习算法。

In order to compute the minimum set of roles of decision table when a new instance is given into a Knowledge Representation System for incremental learning,all the data in the decision table will be recalculated in the classical method. Clearly,this method is not effective.In this paper,a criteria for the minimal recalculation based on the rough sets theory is given, and an improved algorithm for incremental learning is present.The improved algorithm in this paper is better than the classical method in some sense.