A novel hybrid feature selection method considering feature interaction in neighborhood rough set

Wan, Jihong; Yuan, Zhong; Li, Tianrui; Yang, Xiaoling; Sang, Binbin

doi:10.1016/j.knosys.2021.107167

Cited by 63 publications

(15 citation statements)

References 52 publications

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“…However, it is ineffective at recognizing undiscovered network attacks. Recursive feature elimination (RFE) and other feature removal approaches are claimed to be capable of obtaining the most valuable portion of the original data and enhancing the efficacy of network attack identification while lowering computing effort 11 – 13 .…”

Section: Introductionmentioning

confidence: 99%

ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

Ren

Zeng

Cao

et al. 2022

Sci Rep

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Network assaults pose significant security concerns to network services; hence, new technical solutions must be used to enhance the efficacy of intrusion detection systems. Existing approaches pay insufficient attention to data preparation and inadequately identify unknown network threats. This paper presents a network intrusion detection model (ID-RDRL) based on RFE feature extraction and deep reinforcement learning. ID-RDRL filters the optimum subset of features using the RFE feature selection technique, feeds them into a neural network to extract feature information and then trains a classifier using DRL to recognize network intrusions. We utilized CSE-CIC-IDS2018 as a dataset and conducted tests to evaluate the model’s performance, which is comprised of a comprehensive collection of actual network traffic. The experimental results demonstrate that the proposed ID-RDRL model can select the optimal subset of features, remove approximately 80% of redundant features, and learn the selected features through DRL to enhance the IDS performance for network attack identification. In a complicated network environment, it has promising application potential in IDS.

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Section: Introductionmentioning

confidence: 99%

ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

Ren

Zeng

Cao

et al. 2022

Sci Rep

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“…The filter method is independent of the execution of the learner and evaluates the recognition ability of features by using various statistical measurements, such as large margin, 28 mutual information, 29 and rough set. 30 In this article, we focus on the filter method based on rough set due to it is simple and effective.…”

Section: Introductionmentioning

confidence: 99%

“…The embedded approach integrates the process of feature selection into the construction of learning model, so it usually needs iterative matrix inversion calculation, which is time‐consuming. The filter method is independent of the execution of the learner and evaluates the recognition ability of features by using various statistical measurements, such as large margin, 28 mutual information, 29 and rough set 30 . In this article, we focus on the filter method based on rough set due to it is simple and effective.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood rough set based multi‐label feature selection with label correlation

Liu

et al. 2022

Concurrency and Computation

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Summary Neighborhood rough set (NRS) is considered as an effective tool for feature selection and has been widely used in processing high‐dimensional data. However, most of the existing methods are difficult to deal with multi‐label data and are lack of considering label correlation (LC), which is an important issue in multi‐label learning. Therefore, in this article, we introduce a new NRS model with considering LC. First, we explore LC by calculating the similarity relation between labels and divide the related labels into several label subsets. Then, a new neighborhood relation is proposed, which can solve the problem of neighborhood granularity selection by using the nearest neighbor information distribution of instances under the related labels. On this basis, the NRS model is reconstructed by embedding LC information, and the related properties of the model are discussed. Moreover, we design a new feature significance function to evaluate the quality of features, which can well capture the specific relationship between features and labels. Finally, a greedy forward feature selection algorithm is designed. Extensive experiments which are conducted on different types of datasets verify the effectiveness of the proposed algorithm.

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“…1,2 It also has the ability of deduction, 3 reduction, 4 and common sense reasoning. 5,6 In the past decades, rough set theory has been widely applied in the fields of machine learning, knowledge acquisition, decision analysis, knowledge discovery in databases, expert system, and pattern recognition. 7 Formalizing rough sets is a key issue in the research of rough set theory.…”

Section: Introductionmentioning

confidence: 99%

“…Rough set theory, proposed by Z. Pawlak in 1982, is a new mathematical tool to deal with quantitative uncertainty, vagueness, and imprecision information 1,2 . It also has the ability of deduction, 3 reduction, 4 and common sense reasoning 5,6 . In the past decades, rough set theory has been widely applied in the fields of machine learning, knowledge acquisition, decision analysis, knowledge discovery in databases, expert system, and pattern recognition 7 …”

Section: Introductionmentioning

confidence: 99%

Formalizing rough sets using a new noncontingency axiomatic system

Deng

Guan

Wang

et al. 2021

Int J of Intelligent Sys

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Formalization of rough sets is a key issue in rough set theory. When rough sets are formalized by propositional logic, predicate logic, or modal propositional logic, it easily suffers from some problems. For instance, an incomplete system is obtained. The concepts of “ p r e c i s e” or “ r o u g h” of rough sets cannot be described. To tackle these issues, a new noncontingency axiomatic system is proposed for formalizing rough sets in this paper. First, a new concise accessibility relation is defined for the axiomatic system; then, two simpler axiom schemas of the axiomatic system are designed to replace the axiom schema K. This is helpful to prove the soundness and completeness theorems for the axiomatic system. Finally, rough sets can be perfectly formalized by our proposed axiomatic system. Theoretical analysis proves that a complete formal system is achieved. In addition, the concepts of “ p r e c i s e” or “ r o u g h” of rough sets can be described without the help of semantics functions of metalanguage.

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A novel hybrid feature selection method considering feature interaction in neighborhood rough set

Cited by 63 publications

References 52 publications

ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

Neighborhood rough set based multi‐label feature selection with label correlation

Formalizing rough sets using a new noncontingency axiomatic system

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