Compacted decision tables based attribute reduction

Wei, Wei; Wang, Junhong; Liang, Jiye; Mi, Xin; Dang, Chuangyin

doi:10.1016/j.knosys.2015.06.013

Cited by 40 publications

(7 citation statements)

References 44 publications

(55 reference statements)

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“…The decision table classifier works representing a set of conditions needed to determine the occurrence of a group of actions by means of a table format [10]. This technique has also been used in IDS approaches [1] [11].…”

Section: Classification Techniquesmentioning

confidence: 99%

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Data Analysis of Wireless Networks Using Classification Techniques

Canêdo¹,

Romariz²

2019

9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)

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In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of such networks, so that administrators can take action. This work aims to analyze classification techniques in relation to data from Wireless Networks, using some classes of anomalies pre-established according to some defined criteria of the MAC layer. For data analysis, WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The classification algorithms present a success rate in the classification of viable data, being indicated in the use of intrusion detection systems for wireless networks.

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Section: Classification Techniquesmentioning

confidence: 99%

“…Each vertex represents as random variable and each node represents a correlation among the variables [1] [9]. [10]. This technique has also been used in IDS approaches [1] [11].…”

Section: Classification Techniquesmentioning

confidence: 99%

Data Analysis of Wireless Networks Using Classification Techniques

Canêdo¹,

Romariz²

2019

9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)

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“…In this paper, the construction processes of two types of decision tables are compared, and the PRR of a weighted decision table is used to construct the discernibility matrix so as to optimize the VPR algorithm. On the basis of decision tables, compacted decision table [41] is described, and the weighted decision table, which is obtained after the decision table has been compressed, is proposed. The main contribution of this paper is to develop a VRR algorithm for decision tables.…”

Section: Introductionmentioning

confidence: 99%

New Variable Precision Reduction Algorithm for Decision Tables

Li¹,

Xiao²

2023

Preprint

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Variable precision reduction (VPR) and positive region reduction (PRR) are common definitions in attribute reduction. The compacted decision table is an extension of a decision table. In this paper, we propose another extension, called the weighted decision table. In both types of decision tables, VPR is defined, and the corresponding discernibility matrices for the PRR are proposed. Then, algorithms for obtaining the PRR from the discernibility matrices are presented. In both types of decision tables, the relationship between VPR and PRR is established by comparing the corresponding discernibility matrices. If the precision of the VPR meets the given conditions, then the PRR algorithms can be used to obtain the results after modifying some decision values in the decision tables. An analysis of the modification process of the decision values and the compression process of decision tables is used to propose a new algorithm for VPR in decision tables that ensures credibility. The effectiveness of the proposed algorithm was evaluated by an experimental comparison with existing VPR algorithms.

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“…As the core of the rough set [8,23,36,43,44,48], the problem of attribute reduction [9,39,45,52,60] can also be further explored in terms of Triple-G MGRS. However, since obtaining reduct based on Triple-G MGRS requires three different types of the information granulation, the elapsed time of searching reduct may be huge.…”

Section: Introductionmentioning

confidence: 99%

Triple-G: a new MGRS and attribute reduction

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

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Different from classical rough set, Multigranulation Rough Set (MGRS) is frequently designed for approximating target through using multiple results of information granulation. Presently, though many forms of MGRS have been intensively explored, most of them are constructed based on the homogeneous information granulation with respect to different scales or levels. They lack the multi-view which involves the results of heterogeneous information granulation. To fill such a gap, a Triple-G MGRS is developed. Such a Triple-G is composed of three different heterogeneous information granulations:(1) neiGhborhood based information granulation; (2) Gap based information granulation; (3) Granular ball based information granulation. Neighborhood provides a parameterized mechanism while gap and granular ball offer two representative data-adaptive strategies for performing information granulation. Immediately, both optimistic and pessimistic MGRS can be re-constructed. Furthermore, the problem of attribute reduction is also addressed based on the proposed models. Not only the forward greedy searching is used for deriving the Triple-G MGRS related reducts, but also an attribute grouping based accelerator is reported for further speeding up the process of searching reducts. The experimental results over 20 UCI data sets demonstrate the follows: (1) from the viewpoint of the generalization performance, the reducts obtained by our Triple-G MGRS is superior to those obtained by previous researches; (2) attribute grouping does speed up the process of searching reducts.

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Compacted decision tables based attribute reduction

Cited by 40 publications

References 44 publications

Data Analysis of Wireless Networks Using Classification Techniques

Data Analysis of Wireless Networks Using Classification Techniques

New Variable Precision Reduction Algorithm for Decision Tables

Triple-G: a new MGRS and attribute reduction

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