Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets

被引：0

作者：

Fernandez, Alberto ^{[1
]}

Berlanga, Francisco J. ^{[2
]}

del Jesus, Maria J. ^{[3
]}

Herrera, Francisco ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain

[2] Univ Zaragoza, Dept Comp Sci & Syst Engn, Zaragoza, Spain

[3] Univ Jaen, Dept Comp Sci, Jaen, Spain

来源：

PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | 2009年

关键词：

Fuzzy Rule-Based Classification Systems; Genetic Fuzzy Systems; Genetic Programming; Imbalanced Data-Sets; Interpretability; ACCURACY; SYSTEMS; PERFORMANCE; CLASSIFIERS;

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：

Classification in imbalanced domains is an important problem in Data Mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. The aim of this work is to study the behaviour of the GP-COACH algorithm in the scenario of data-sets with high imbalance, analysing both the performance and the interpretability of the obtained fuzzy models. To develop the experimental study we will compare this approach with a well-known fuzzy rule learning algorithm, the Chi et al.'s method, and an algorithm of reference in the field of imbalanced data-sets, the C4.5 decision tree.

引用

页码：42 / 47

页数：6

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