Nearest Neighbor Condensation Based on Fuzzy Rough Set for Classification

被引：1

作者：

Pan, Wei ^{[1
]}

She, Kun ^{[1
]}

Wei, Pengyuan ^{[1
]}

Zeng, Kai ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Scicence & Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014 | 2014年 / 8818卷

关键词：

nearest neighbor rule; training-set; consistent subset; fuzzy rough set; lower approximation; classification; REDUCTION; RULES; RISK;

D O I：

10.1007/978-3-319-11740-9_40

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work introduces a novel algorithm, called Condensation rule based on Fuzzy Rough Sets (FRSC), based on the FCNN rule together with fuzzy rough sets theory, to compute training-set-consistent subset for the nearest neighbor decision rule. In combination with fuzzy rough set theory, the FRSC rule improves the performance of FCNN rule. Two variants, named as FRSC1 and FRSC2, of the FRSC rule, are presented. The FRSC1 rule is suitable for small data set and the FRSC2 adapts to larger data sets. Compared with the FCNN rule, the FRSC1 rule requires much less time cost and gets smaller subset for small data set. For medium-size data set, less than 5000 samples, the FRSC2 rule has better time performance than FCNN rule.

引用

页码：432 / 443

页数：12

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