On representation of fuzzy measures for learning Choquet and Sugeno integrals

被引:17
|
作者
Beliakov, Gleb [1 ]
Divakov, Dmitriy [2 ]
机构
[1] Deakin Univ, Sch Informat Technol, Geelong, Vic 3220, Australia
[2] RUDN Univ, Peoples Friendship Univ Russia, 6 Miklukho Maklaya St, Moscow 117198, Russia
关键词
Aggregation functions; Fuzzy measures; Capacities; Choquet integral; Sugeno integral; Multicriteria decision making; AGGREGATION;
D O I
10.1016/j.knosys.2019.105134
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper examines the marginal contribution representation of fuzzy measures, used to construct fuzzy measure from empirical data through an optimization process. We show that the number of variables can be drastically reduced, and the constraints simplified by using an alternative representation. This technique makes optimizing fitting criteria more efficient numerically, and allows one to tackle learning problems with higher number of correlated decision criteria. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页数:5
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