A Genetic Algorithm for Feature Selection and Granularity Learning in Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-Sets

被引：0

作者：

Villar, Pedro ^{[1
]}

Fernandez, Alberto ^{[2
]}

Herrera, Francisco ^{[2
]}

机构：

[1] Univ Granada, Dept Software Engn, ETS Ing lnformat & Telecomun, E-18071 Granada, Spain

[2] Univ Granada, Dept Comp Sci, ETS Ing lnformat & Telecomun, Granada, Spain

来源：

INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND METHODS, PT 1 | 2010年 / 80卷

关键词：

Fuzzy Rule-Based Classification Systems; imbalanced data-sets; Genetic Algorithms; feature selection; granularity level;

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：

This contribution proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact and precise models by selecting the adequate variables and adapting the number of fuzzy labels for each problem.

引用

页码：741 / +

页数：2

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