属性重要性评分方法的改进

Improvement on Attribute Significance Evaluation Method

研究信息系统的属性重要性评分方法,通过引入敏感系数构建神经网络模型,提出属性重要性评分算法,将信息系统的各条件属性和决策属性构造一个径向基函数(RBF)神经网络。经训练和学习后,综合考虑各属性间的关系,动态调整RBF网络的拓扑结构,评分各属性的重要性。以红籽西瓜性状数据作为样本数据和测试数据进行实例分析,验证该方法的有效性。

Method for attribute significance evaluation is researched in information system.Attribute significance evaluation algorithm is proposed based on sensitivity coefficient and RBF neural network.Information system about condition attributes and decision-making attributes is constructed as a RBF neural network.Attribute significance is determined by RBF neural network training and learning,which analyzes sensitivity between network output and the input.After dynamic adjustment of RBF network topology and judge importance of attribute,the method based on sensitivity coefficient and RBF neural network is proved through the example of red-seed watermelon,which shows the algorithm is effective.