Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection

2010 ◽  
Vol 7 (1) ◽  
pp. 45-64 ◽  
Author(s):  
Oscar Cordón ◽  
Arnaud Quirin
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Fuzzy Rule ◽  
Rule Based

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

Fuzzy Rule-Based Layered Classifier and Entropy-Based Feature Selection for Intrusion Detection System

2021 ◽  
pp. 289-309
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fuzzy Rule ◽  
Intrusion Detection Systems ◽  
Rule Based ◽  
Detection Systems ◽  
Positive Rate ◽  
Rule Based Classifier

Intrusion detection systems must detect the vulnerability consistently in a network and also perform efficiently with the huge amount of traffic. Intrusion detection systems must be capable of detecting emerging and proactive threats in the networks. Various classifiers are used to classify the threats as normal or intrusive by supervising the system activity. In this chapter, layered fuzzy rule-based classifier is proposed to detect the various intrusions, and fuzzy entropy-based feature selection is proposed to identify the relevant features. Layered fuzzy rule-based classifier is proposed to improve the performance of the intrusion detection system. KDD dataset contains various attacks; these attacks are grouped into four classes, namely Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Real-time dataset is also considered in this research. Experimental result shows that the proposed method provides good detection rate, minimizes the false positive rate, and less computational time.

Sign in / Sign up

Export Citation Format

Share Document