Rule Extraction Based on Data Dimensionality Reduction Using RBF Neural Networks

摘要：

Compact rules is desirable in the task of rule extraction. Since there are often redundant or irrelevant attributes in data sets, removing the redundant or irrelevant attributes from the data sets can lead to more compact rules. In this paper, firstly, a novel method, a separability-correlation measure (SCM), is used to rank the importance of attributes, and an RBF classifier is used to evaluate the best subset of attributes to be retained. Secondly, large overlaps between clusters of the same class are allowed in order to reduce the number of hidden units in the RBF network. Thirdly, rule extraction is carried out based on the retained subset of attributes used as the input to the RBF neural network. Simulations show that this procedure lead to more compact rules.

作者: Xiuju Fu Lipo Wang

作者单位: School of Electrical and Electronic Engineering Nanyang Technological University Block S2, Nanyang Avenue Singapore 639798

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 187-191

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）