A Taxation Attribute Reduction Based on Genetic Algorithm and Rough Set Theory

摘要：

Selection of Taxation attributes is one difficult question in analyzing the sources of taxation.This paper introduces genetic-algorithm-based rough set attribute reduction algorithm into the job of taxation attribute reduction.By referring to the concept of dependability in rough set,this method optimizes the configuration of fitness function,improves the convergence of original algorithm and changes the limitation of current attribute reduction in genetic algorithm.This algorithm fundamentally realizes the selection of comparatively small attribute sets with the presupposition that the data classification ability is not changed.It is valid after being tested.

作者: Xu Linzhang Han Zhen Zhang Yanning

作者单位: College of Computer,Northwestern Polytechnical University,Xi an,Shaanxi 710072

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）