Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems

2001 ◽  
Vol 136 (1-4) ◽  
pp. 135-157 ◽  
Author(s):  
J Casillas ◽  
O Cordón ◽  
M.J Del Jesus ◽  
F Herrera
Keyword(s):  
Feature Selection ◽  
Learning Process ◽  
Classification System ◽  
Fuzzy Rule ◽  
High Dimensional ◽  
Rule Based ◽  
Genetic Feature ◽  
Genetic Feature Selection

scholarly journals ON DESIGNING FUZZY RULE-BASED MULTICLASSIFICATION SYSTEMS BY COMBINING FURIA WITH BAGGING AND FEATURE SELECTION

2011 ◽  
Vol 19 (04) ◽  
pp. 589-633 ◽  
Author(s):  
KRZYSZTOF TRAWIŃSKI ◽  
OSCAR CORDÓN ◽  
ARNAUD QUIRIN
Keyword(s):  
Feature Selection ◽  
Learning Algorithm ◽  
Rule Learning ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Classification Rule ◽  
High Dimensional ◽  
Global Approach ◽  
Rule Based ◽  
Exhaustive Study

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature selection to design a final FURIA-based fuzzy multiclassifier dealing with high dimensional data. Several parameter settings for the global approach are tested when applied to twenty one popular UCI datasets. The results obtained show that FURIA-based fuzzy multiclassifiers outperform the single FURIA classifier and are competitive with C4.5 multiclassifiers and random forests.

Deep Fuzzy Rule-based Classification System with Improved Wang-Mendel Method

2021 ◽  
pp. 1-1
Author(s):  
Yuangang Wang ◽  
Haoran Liu ◽  
Wenjuan Jia ◽  
Shuo Guan ◽  
Xiaodong Liu ◽  
...  
Keyword(s):  
Classification System ◽  
Fuzzy Rule ◽  
Rule Based
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