How to build aggregation operators from data

被引：85

作者：

Beliakov, G ^{[1
]}

机构：

[1] Deakin Univ, Sch Informat Technol, Burwood 3125, Australia

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2003年 / 18卷 / 08期

关键词：

D O I：

10.1002/int.10120

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article discusses a range of regression techniques specifically tailored to building aggregation operators from empirical data. These techniques identify optimal parameters of aggregation operators from various classes (triangular norms, uninorms, copulas, ordered weighted aggregation (OWA), generalized means, and compensatory and general aggregation operators), while allowing one to preserve specific properties such as commutativity or associativity. (C) 2003 Wiley Periodicals, Inc.

引用

页码：903 / 923

页数：21

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