基于粗糙集和神经网络理论的规则提取算法

Algorithm on rule extraction based on rough set and neural network theory

提出了一种基于粗糙集和神经网络组合进行规则提取的方法。首先对初始数据集进行离散化,并利用粗糙集对决策表中的条件属性进行初步约简,然后利用神经网络对数据进行学习和预测,并通过删除网络不能分类的数据来对决策表中的噪声进行过滤,最后再由粗糙集值约简算法进行规则提取。实验表明,该方法相对于传统规则提取算法快速有效,在保留神经网络高鲁棒性的同时,避免了从神经网络中提取规则的困难。

This paper proposes a method for rule extraction based on rough set and neural network.Firstly,this paper disperses initial data set and initiative reduces condition attributes of decision-making table using rough set,then learns and forecasts data using neural network and filtrates yawp of decision-making table through deleting unclassified data,finally reduces rules using value reduction algorithm of rough set.The experiment proves that this method is quick and effective,and can remain high robustness of neural network avoiding the difficulty to extract rules from neural network compared to traditional rule extraction algorithms.