Partial similarity measure of uncertain random variables and its application to portfolio selection

被引：4

作者：

Gao, Rong ^{[1
]}

Ahmadzade, Hamed ^{[2
]}

Rezaei, Kamran ^{[3
]}

Rezaei, Hassan ^{[3
]}

Naderi, Habib ^{[2
]}

机构：

[1] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China

[2] Univ Sistan & Baluchestan, Dept Math Sci, Zahedan, Iran

[3] Univ Sistan & Baluchestan, Dept Comp Sci, Zahedan, Iran

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2020年 / 39卷 / 01期

关键词：

Chance theory; uncertain random variable; partial similarity measure; portfolio selection; pattern recognition; VAGUE SETS; DISTANCE; ENTROPY;

D O I：

10.3233/JIFS-190942

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A similarity measure determines the similarity between two objects. As important roles of similarity measure in chance theory, this paper introduces the concept of partial similarity measure for two uncertain random variables. Based on maximum similarity principle, partial similarity measure are used to recognize pattern problems. As an application in finance, partial similarity measure is applied to optimize portfolio selection of uncertain random returns via Monte-Carlo simulation and craw search algorithm.

引用

页码：155 / 166

页数：12

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