Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System

2009 ◽  
pp. 294-301 ◽  
Author(s):  
Alberto Fernández ◽  
María José del Jesus ◽  
Francisco Herrera
Keyword(s):  
Imbalanced Data ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Data Sets ◽  
Inference System ◽  
Rule Based ◽  
Imbalanced Data Sets ◽  
Adaptive Inference

A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets

2008 ◽  
Vol 159 (18) ◽  
pp. 2378-2398 ◽  
Author(s):  
Alberto Fernández ◽  
Salvador García ◽  
María José del Jesus ◽  
Francisco Herrera
Keyword(s):  
Imbalanced Data ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Data Sets ◽  
Rule Based ◽  
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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