AN METHOD TO EXTRACT COMPREHENSIBLE RULES

摘要：

Knowledge acquisition has been a bottleneck when constructing expert system.Neural network has many advantages to obtain knowledge in the application field.However, the major drawback of neural network is to be lack of comprehensibility.Knowledge obtained by network is concealed in the architecture of neural networks and weights between neurons.Rule extraction from neural network has been recognized one of the proper methods to deal with this drawback.This paper developed researches on rule extraction.For problems with continuous-valued and discrete-valued attributes, the paper presents an approach to extract understandable and concise rules.Rules extracted are comprehensible not only for discrete value but also for continuous value.Our experiment results on real-word dataset validate our approach and show that rules extracted by our approach are comprehensible and concise.

关键词： Rule extraction Continuous-valued attribute Neural network

作者: PING GUO JING CHEN SHENG-JUN SUN

作者单位: School of Computer Science, Chongqing University, Chongqing, 400044, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 808-812

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）