Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models

被引：8

作者：

Saleh, Emran ^{[1
]}

Valls, Aida ^{[1
]}

Moreno, Antonio ^{[1
]}

Romero-Aroca, Pedro ^{[2
]}

Torra, Vicenc ^{[3
]}

Bustince, Humberto ^{[4
]}

机构：

[1] Univ Rovira & Virgili, Dept Engn Informat & Matemat, Tarragona, Spain

[2] Univ Rovira & Virgili, Univ Hosp St Joan De Reus, Inst Invest Sanitaria Pere Virgili IISPV, Ophthalm Serv, Reus, Spain

[3] Univ Skovde, Sch Informat, Skovde, Sweden

[4] Univ Publ Navarra, Inst Smart Cities, Dept Automat & Computac, Pamplona, Spain

来源：

MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE (MDAI 2018) | 2018年 / 11144卷

关键词：

Fuzzy measures; Aggregation functions; Choquet integral; Sugeno integral; Fuzzy rule-based systems; Diabetic retinopathy; DIABETIC-RETINOPATHY; INTEGRALS;

D O I：

10.1007/978-3-030-00202-2_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno lambda-measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature.

引用

页码：114 / 127

页数：14

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