Imbalanced datasets classification by fuzzy rule extraction and genetic algorithms

被引：0
|
作者：
Soler, Vicenc
Cerquides, Jesus
Sabria, Josep
Roig, Jordi
Prim, Marta
机构：
[1] Univ Autonoma Barcelona, Dept Microelect & Sist Electron, E-08193 Barcelona, Spain
[2] Univ Barcelona, Dept MAIA, WAI, Volume Visualizat & Artificial Intelligence Res G, E-08007 Barcelona, Spain
[3] Hosp Univ Dr Josep Trueta, Dept Gynecol & Obstet, E-17002 Girona, Spain
来源：
ICDM 2006: SIXTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, WORKSHOPS | 2006年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose a method based on the extraction of fuzzy rules by genetic algorithms 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 recombined to obtain 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
收藏
页码：330 / 334
页数：5
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