From Data to Granular Data and Granular Classifiers

被引：0

作者：

Al-Hmouz, Rami ^{[1
]}

Pedrycz, Witold ^{[1
,2
,3
]}

Balamash, Abdullah ^{[1
]}

Morfeq, Ali ^{[1
]}

机构：

[1] King Abdulaziz Univ, Dept Elect & Comp Engn, Jeddah 21413, Saudi Arabia

[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada

[3] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2014年

关键词：

pattern classification; information granules; Granular Computing; clustering; principle of justifiable granularity; Fuzzy C-Means;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Information granules emerging as a result of an abstract and more condensed and global view at numeric data play an essential role in various pattern recognition pursuits. In this study, we investigate an idea of granular prototypes (representatives) and discuss their role in the realization of classification schemes. A two-stage procedure of a formation of information granules is discussed. We show how the commonly used clustering methods are viewed as a prerequisite for the construction of granular prototypes. In this regard, a certain version of the principle of justifiable granularity is investigated. In the sequel, a characterization of information granules expressed in terms of their information (classification) content is provided and its usage in the realization of a classifier is studied. Experimental studies involving both synthetic and publicly available data are reported.

引用

页码：432 / 438

页数：7

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