An application of feature selection for the fuzzy rule based classifier design with the order based sematics of linguistic terms for high-dimensional datasets

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.

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A Co-optimization PSO for Fuzzy Rule-Based Classifier Design Problem Based on Enlarged Hedge Algebras

Periodica Polytechnica Electrical Engineering and Computer Science ◽

10.3311/ppee.16141 ◽

2021 ◽

Author(s):

Du Duc Nguyen ◽

Phong Dinh Pham

Keyword(s):

Design Problem ◽

Fuzzy Rule ◽

Pso Algorithm ◽

Computational Semantics ◽

Two Phase ◽

Rule Based ◽

Classifier Design ◽

Phase Optimization ◽

Rule Based Classifier ◽

Parameter Values

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers.

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

Handbook of Research on Cyber Crime and Information Privacy - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-5728-0.ch015 ◽

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.

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An Intelligent Fuzzy Rule based Feature Selection for Effective Intrusion Detection

2018 International Conference on Recent Trends in Advance Computing (ICRTAC) ◽

10.1109/icrtac.2018.8679328 ◽

2018 ◽

Author(s):

Riyaz. B ◽

Ganapathy. S

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Fuzzy Rule ◽

Rule Based ◽

Selection For

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A HEDGE ALGEBRAS BASED REASONING METHOD FOR FUZZY RULE BASED CLASSIFIER

Vietnam Journal of Science and Technology ◽

10.15625/2525-2518/57/5/13811 ◽

2019 ◽

Vol 57 (5) ◽

pp. 631

Author(s):

Phạm Đình Phong ◽

Nguyễn Đức Dư ◽

Hoàng Văn Thông

Keyword(s):

Fuzzy Sets ◽

Design Method ◽

Fuzzy Rule ◽

Design Methods ◽

Rule Based ◽

Classifier Design ◽

Real World Datasets ◽

Rule Based Classifier ◽

Rule Bases ◽

Sets Theory

The fuzzy rule based classifier (FRBC) design methods have intensively been being studied during last years. The ones designed by utilizing hedge algebras as a formalism to generate the optimal linguistic values along with their (triangular and trapezoidal) fuzzy sets based semantics for the FRBCs have been proposed. Those design methods generate the fuzzy sets based semantics because the classification reasoning method still bases on the fuzzy set theory. One question which has been arisen is whether there is a pure hedge algebras classification reasoning method so that the fuzzy sets based semantic of the linguistic values in the fuzzy rule bases can be replaced with the hedge algebras based semantic. This paper answers that question by presenting a fuzzy rule based classifier design method based on hedge algebras with a pure hedge algebras classification reasoning method. The experimental results over 17 real world datasets are compared to the existing methods based on hedge algebras and fuzzy sets theory showing that the proposed method is effective and produces good results.

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