ATTRIBUTE REDUCTION BASED ON ATTRIBUTE SIMILARITY AND WITH APPLICATION TO LOGGING INTERPRETATION
Due to the explosive growth of electronically stored information, automatic methods must be developed to maintain and use this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents a novel approach of attribute reduction based on attribute similarity, to greatly reduce this data redundancy. The work is applied to the quantitativecomputation of reservoir parameter, considerably reducing dimensionality with minimal loss of information.Experimental results show that the method used in this paper is not only more powerful but also finer than the conventional rough set-based approach, and it has high fitting precision and quick rate of convergence.
Attribute reduction attribute similarity attribute significance quantitative computation reservoir parameter
CHANG-BIAO LI KE-WEN XIA JIAN-PING SONG XIAO-FANG YUAN
School of Electronic & information Engineering, Xian Jiaotong University, Xian 710049, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1382-1387
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）