scholarly journals Feature Selection for Multi-Class Imbalanced Data Sets Based on Genetic Algorithm

2015 ◽  
Vol 2 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Li-min Du ◽  
Yang Xu ◽  
Hua Zhu
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Imbalanced Data ◽  
Data Sets ◽  
Imbalanced Data Sets ◽  
Selection For

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets

2013 ◽  
Vol 43 (1) ◽  
pp. 332-346 ◽  
Author(s):  
Nicolas Garcia-Pedrajas ◽  
Javier Pérez-Rodríguez ◽  
Aida de Haro-García
Keyword(s):  
Imbalanced Data ◽  
Instance Selection ◽  
Data Sets ◽  
Imbalanced Data Sets ◽  
Selection For

Online Multi-label Feature Selection on Imbalanced Data Sets

2018 ◽  
pp. 165-174
Author(s):  
Jing Liu ◽  
Zhongwen Guo ◽  
Zhongwei Sun ◽  
Shiyong Liu ◽  
Xupeng Wang
Keyword(s):  
Feature Selection ◽  
Imbalanced Data ◽  
Data Sets ◽  
Imbalanced Data Sets

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

2012 ◽  
Vol 20 (03) ◽  
pp. 369-397 ◽  
Author(s):  
PEDRO VILLAR ◽  
ALBERTO FERNÁNDEZ ◽  
RAMÓN A. CARRASCO ◽  
FRANCISCO HERRERA
Keyword(s):  
Feature Selection ◽  
Statistical Tests ◽  
Imbalanced Data ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Data Sets ◽  
Rule Based ◽  
Imbalanced Data Sets ◽  
Wide Range ◽  
Fuzzy Labels

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.

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