A variable precision rough set approach to knowledge discovery in land cover classification

作     者：Iftikhar U.Sikder 

作者机构：Department of Information SystemCleveland State UniversityOHUSA Department of Electrical Engineering and Computer ScienceCleveland State UniversityOHUSA 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2016年第9卷第12期

页      面：1206-1223页

核心收录：

学科分类：07[理学] 0708[理学-地球物理学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

主　　题：Rough set theory soft computing granular computing remote sensing approximate reasoning 

摘      要：This paper presents a granular computing approach to spatial classification and prediction of land cover classes using rough set variable precision *** particular,it presents an approach to characterizing large spatially clustered data sets to discover knowledge in multi-source supervised *** evidential structure of spatial classification is founded on the notions of equivalence relations of rough set *** allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary *** paper also identifies how approximate reasoning can be introduced by using variable precision rough sets in the context of land cover *** rough set theory is applied to demonstrate an empirical application and the predictive performance is compared with popular baseline machine learning algorithms.A comparison shows that the predictive performance of the rough set rule induction is slightly higher than the decision tree and significantly outperforms the baseline models such as neural network,naïve Bayesian and support vector machine methods.