Spatial feature selection method based on maximum entropy theory

CORC > 北京大学 > 信息科学技术学院

	Spatial feature selection method based on maximum entropy theory
	Song, Guo-Jie ; Tang, Shi-Wei ; Yang, Dong-Qing ; Wang, Teng-Jiao
刊名	ruan jian xue baojournal of software
	2003
英文摘要	Feature selection has an important application in the field of pattern recognition and data mining etc. However, in real world domains, if there are spatial data operated in the application, the performance of feature selection will be decreased because of without considering the characteristic of spatial data. A feature selection method from the point of the characteristic of spatial data, named MEFS (maximum entropy feature selection), is proposed. Based on the theory of maximum entropy, MEFS uses mutual information and Z-test technologies, and takes two-step method to execute feature selection. The first step is predicate selection, and the second step is to choose relevant dataset corresponding to each predicate. At last, the experiments between feature selection algorithms MEFS and RELIEF, and between ID3 classification algorithm and classification algorithm based on MEFS are carried out. The experimental results show that the MEFS algorithm not only saves feature selection and classification time, but also improves the quality of classification.; EI; 0; 9; 1544-1550; 14
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294076]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Song, Guo-Jie,Tang, Shi-Wei,Yang, Dong-Qing,et al. Spatial feature selection method based on maximum entropy theory[J]. ruan jian xue baojournal of software,2003.
APA	Song, Guo-Jie,Tang, Shi-Wei,Yang, Dong-Qing,&Wang, Teng-Jiao.(2003).Spatial feature selection method based on maximum entropy theory.ruan jian xue baojournal of software.
MLA	Song, Guo-Jie,et al."Spatial feature selection method based on maximum entropy theory".ruan jian xue baojournal of software (2003).