FEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETS

被引：22

作者：

Villar, Pedro ^{[1
]}

Fernandez, Alberto ^{[3
]}

Carrasco, Ramon A. ^{[1
]}

Herrera, Francisco ^{[2
]}

机构：

[1] Univ Granada, ETSIIT, Dept Software Engn, E-18071 Granada, Spain

[2] Univ Granada, ETSIIT, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain

[3] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain

来源：

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS | 2012年 / 20卷 / 03期

关键词：

Fuzzy rule-based classification systems; imbalanced data-sets; genetic algorithms; feature selection; granularity level; NEURAL-NETWORKS; INFORMATION GRANULATION; STATISTICAL COMPARISONS; CLASSIFIERS; PERFORMANCE; STRATEGIES; ACCURACY; DESIGN; AREA;

D O I：

10.1142/S0218488512500195

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly imbalanced data-sets. 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 models by selecting the adequate variables and adapting the number of fuzzy labels for each problem, improving the interpretability of the model. The experimental analysis is carried out over a wide range of highly imbalanced data-sets and uses the statistical tests suggested in the specialized literature.

引用

收藏

页码：369 / 397

页数：29

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