文献类型：期刊文献

英文题名：Kernel Neighborhood Rough Sets Model and Its Application

作者：Zeng, Kai Jing, Siyuan

第一作者：曾凯

通信作者：Zeng, K[1]

机构：[1]Guizhou Inst Technol, Sch Data Sci, 1 Caiguan Rd, Guiyang 550003, Guizhou, Peoples R China;[2]Leshan Normal Univ, Sch Comp Sci, Binhe Rd, Leshan 614000, Sichuan, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Inst Technol, Sch Data Sci, 1 Caiguan Rd, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院;

年份：2018

卷号：2018

外文期刊名：COMPLEXITY

收录：;EI(收录号：20191106643941);Scopus(收录号：2-s2.0-85062835136);WOS:【SCI-EXPANDED(收录号:WOS:000443629600001)】；

基金：This work is supported by the National Natural Science Foundation of China (Grant no. 61702128) and the Foundation of the Guizhou Institute of Technology (Grant no. KJZX17-003).

语种：英文

外文关键词：Data mining - Fuzzy set theory - Pattern recognition - Fuzzy systems

摘要：Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called kernel neighborhood rough sets, which integrates the advantages of the neighborhood and kernel models. Moreover, the model is used in the problem of feature selection. The proposed method is tested on the UCI datasets. The results show that our model outperforms classic models.