Framework for Efficient Letter Selection in Genetic Algorithm Based Data Mining

摘要：

We present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of letter selection.Data mining involves nontrivial process of extracting knowledge or patterns from large databases.Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining.In this paper we propose a letter selection in genetic algorithm based data mining,Explicit letter selection is traditionally done as a wrapper approach where every candidate feature subset is evaluated by executing the data mining algorithm on that subset.In this article we present a GA for doing both the tasks of mining and letter selection simultaneously by evolving a binary code along side the chromosome structure used for evolving the rules.Results from applying the above techniques to a real world data mining problem show that combining both the letter selection methods provides the best performance in terms of prediction accuracy and computational efficiency.

关键词： letter selection GA data mining framework self-adaptive

作者: Xiaoyan Chen Shijue Zheng Tao Tao

作者单位: Department of Computer Science,Hua Zhong Normal University,Wuhan,Hubei,430079,China Sanya Garrison,Sanya,Hainan,572011,China

会议类型: 国际会议

会议名称: 2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会(2008 International Symposium on Distributed Computing and Applications for Business Engineering and Science)

会议地点: 大连

会议语种:英文

页码: 334-338

在线出版日期: 2008-07-27（万方平台首次上网日期，不代表论文的发表时间）