scholarly journals A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems

2003 ◽  
pp. 315-326 ◽  
Author(s):  
O. Cordón ◽  
F. Herrera ◽  
M.J. del Jesus ◽  
L. Magdalena ◽  
A.M. Sánchez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Data Base ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Rule Based ◽  
Multiobjective Genetic Algorithm

Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm

2021 ◽  
pp. 1-7
Author(s):  
Zahra Asghari Varzaneh ◽  
Marjan Kuchaki Rafsanjani
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Classification System ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Classification Rate ◽  
Rule Based

Intrusion can compromise the integrity, confidentiality, or availability of a computer system. Intrusion Detection System (IDS) is a type of security software designed to monitor network traffic and identify network intrusions. In this paper, A Fuzzy Rule – Based classification system is used to detect intrusion in a computer network. In order to improve the classification rate, a new method is proposed based on Genetic Algorithm (GA) for rule weights specification. The proposed method is tested on KDD99 dataset. Experimental results show the proposed method improves the performance of the fuzzy rule-based classification systems in terms of detection rate and false alarm rate.

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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