A Boolean function approach to feature selection in consistent decision information systems

Kahramanli, Sirzat; Hacıbeyoğlu, Mehmet; Arslan, Ahmet

doi:10.1016/j.eswa.2011.01.002

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…After that, optimal feature subsets will be evaluated and selected with the BP algorithm. The comprehensive description of the DFBFS method that is applied in the first stage can be found in [10]; a brief description is provided in this section.…”

Section: Discernibility Function Based Feature Selection Methodsmentioning

confidence: 99%

“…One of the goals of this study is to compute all MSs and find the best MSs without the risk of losing the optimal ones. Because this is a problem with high computational complexity, a new method, the Decision Relative Discernibility Function-Based FS that is proposed by Kahramanli et al [10], is applied. This method obtains the same results faster and with less memory required than the regular Rough Set method.…”

Section: Discernibility Function Based Feature Selection Methodsmentioning

confidence: 99%

“…The paper is organized as follows: a description of the Discernibility Function Based Feature Selection Method [10] is presented in Sect. 2.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, a wrapper based FS is applied by using the Discernibility Function introduced in [10] as the search algorithm to create candidate feature subsets and the BackPropagation (BP) algorithm to evaluate the feature subsets. Then, the C4.5 (decision tree) and BP algorithms, which are widely used in IDS related papers and show good classification rates [11]- [17] are applied.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Akyol

Hacıbeyoğlu

Karlık

2016

IEICE Trans. Inf. & Syst.

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SUMMARYWith the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99

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Section: Discernibility Function Based Feature Selection Methodsmentioning

confidence: 99%

Section: Discernibility Function Based Feature Selection Methodsmentioning

confidence: 99%

“…The paper is organized as follows: a description of the Discernibility Function Based Feature Selection Method [10] is presented in Sect. 2.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Akyol

Hacıbeyoğlu

Karlık

2016

IEICE Trans. Inf. & Syst.

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“…As a useful tool to deal with covering data, covering-based rough sets have been attracting more and more research interest [1,5,19,41], and in their returns these lead to many interesting and significant problems. For example, from the theoretical point of view, covering approximation models have been constructed [37,42], covering axiomatic systems have been established [17,39], covering reduction problems have been defined [10,21], and covering decision systems have been proposed [11,29]. From the viewpoint of the application, covering-based rough sets have been used in knowledge reduction [3,13].…”

Section: Introductionmentioning

confidence: 99%

Four matroidal structures of covering and their relationships with rough sets

Wang

Zhu

et al. 2013

International Journal of Approximate Reasoning

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Covering is a common form of data representation, and covering-based rough sets serve as an efficient technique to process this type of data. However, many important problems such as covering reduction in covering-based rough sets are NP-hard so that most algorithms to solve them are greedy. Matroids provide well-established platforms for greedy algorithm foundation and implementation. Therefore, it is necessary to integrate covering-based rough set with matroid. In this paper, we propose four matroidal structures of coverings and establish their relationships with rough sets. First, four different viewpoints are presented to construct these four matroidal structures of coverings, including 1-rank matroids, bigraphs, upper approximation numbers and transversals. The respective advantages of these four matroidal structures to rough sets are explored. Second, the connections among these four matroidal structures are studied. It is interesting to find that they coincide with each other. Third, a converse view is provided to induce a covering by a matroid. We study the relationship between this induction and the one from a covering to a matroid. Finally, some important concepts of covering-based rough sets, such as approximation operators, are equivalently formulated by these matroidal structures. These interesting results demonstrate the potential to combine covering-based rough sets with matroids.

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Functions with Continuous Variables

Sasao

2023

Classification Functions for Machine Learning and Data Mining

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A Boolean function approach to feature selection in consistent decision information systems

Cited by 11 publications

References 18 publications

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Four matroidal structures of covering and their relationships with rough sets

Functions with Continuous Variables

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