A Method to Classify Data by Fuzzy Rule Extraction from Imbalanced Datasets

被引：0

作者：

Soler, Vicenc ^{[1
]}

Cerquides, Jesus ^{[2
]}

Sabria, Josep ^{[3
]}

Roig, Jordi ^{[1
]}

Prim, Marta ^{[1
]}

机构：

[1] Univ Autonoma Barcelona, Dept Microelect & Sistemes Elect, Barcelona, Spain

[2] Univ Barcelona, WAI Res Grp, Dept Anay & Appl Math, E-08007 Barcelona, Spain

[3] Hosp Univ Dr Josep Trueta, Dept Gynecol & Obstet, Barcelona, Spain

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT | 2006年 / 146卷

关键词：

Fuzzy Logic; Genetic Algorithms; Down's syndrome; Fuzzy Rule Extraction; Imbalanced Datasets;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a method based on fuzzy, rules for the classification of imbalanced datasets when understandability is an issue. We propose a new method for fuzzy variable construction based on modifying the set of fuzzy variables obtained by the RecBF/DDA algorithm. Later, these variables are combined into fuzzy rules by means of a Genetic Algorithm. The method has been developed for the detection of Down's syndrome in fetus. We provide empirical results showing its accuracy for this task. Furthermore, we provide more generic experimental results over UCI datasets proving that the method can have a wider applicability.

引用

页码：55 / +

页数：2

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