Bitopological approximation space with application to data reduction in multi-valued information systems

Salama, A. S.

doi:10.2298/fil2001099s

Cited by 18 publications

(14 citation statements)

References 22 publications

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“…Collaborative Filtering [30], Variable Precision Rough Set Model, [31], topological rough application [32], reduction in multi-valued information system [33] and so on. A significant application of RST is in the reduction of attributes in databases (feature selection).…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Eid¹,

Medhat²,

Ali³

2023

Computer Systems Science and Engineering

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An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information; nonetheless, investigating all available combinations of attributes will result in some problems. Furthermore, because attribute reduction is primarily a mathematical issue, technical progress in reduction is dependent on the advancement of mathematical models. Because the focus of this study is on the mathematical side of attribute reduction, we propose some methods to make a reduction for information systems according to classical rough set theory, the strength of rules and similarity matrix, we applied our proposed methods to several examples and calculate the reduction for each case. These methods expand the options of attribute reductions for researchers.

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Section: Introductionmentioning

confidence: 99%

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Eid¹,

Medhat²,

Ali³

2023

Computer Systems Science and Engineering

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“…Studying the interaction between topology and rough set theory was the main target for many articles such as [2,19,25,26,28,39,40,48]. This path of study also included some topology's extensions such as minimal structure [15,17] and bitopology [36]. Hybridization of rough sets with some uncertainty tools such as soft and fuzzy sets was investigated in [32,34].…”

Section: Introductionmentioning

confidence: 99%

Topological approach to generate new rough set models

Al-shami

2022

Complex Intell. Syst.

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In this paper, we introduce a topological method to produce new rough set models. This method is based on the idea of “somewhat open sets” which is one of the celebrated generalizations of open sets. We first generate some topologies from the different types of $$N_\rho $$ N ρ -neighborhoods. Then, we define new types of rough approximations and accuracy measures with respect to somewhat open and somewhat closed sets. We study their main properties and prove that the accuracy and roughness measures preserve the monotonic property. One of the unique properties of these approximations is the possibility of comparing between them. We also compare our approach with the previous ones, and show that it is more accurate than those induced from open, $$\alpha $$ α -open, and semi-open sets. Moreover, we examine the effectiveness of the followed method in a problem of Dengue fever. Finally, we discuss the strengths and limitations of our approach and propose some future work.

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“…In recent years, it has been exploited some topological generalizations such as infra topology Al-shami and Mhemdi ( 2022 ), minimal structure (Azzam et al. 2020 ; El-Sharkasy 2021 ) and bitopology Salama ( 2020 ) to deal with rough set concepts and address some medical problems. Following this line, we suggest novel kinds of rough set models inspired by another abstract structure called “supra-topology”.…”

Section: Introductionmentioning

confidence: 99%

Rough sets models inspired by supra-topology structures

Al-shami

Alshammari

2022

Artif Intell Rev

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Our aim of writing this manuscript is to found novel rough-approximation operators inspired by an abstract structure called “supra-topology”. This approach is more relaxed than topological ones and extends the scope of applications because an intersection condition of topology is dispensed. Firstly, we generate eight types of supra-topologies using -neighborhood systems induced from any arbitrary relation. We elucidate the relationships between them and investigate the conditions under which some of them are identical. Then, we create new rough sets models from these supra-topologies and present the main characterizations of their lower and upper approximations. We apply these approximations to classify the regions of the subset and compute its accuracy measures. The master merits of the current approach are to produce the highest accuracy values compared with all approaches given in the published literature under a reflexive relation as well as preserve the monotonicity property of accuracy and roughness measures. Moreover, we demonstrate the good performance of the followed technique through analysis of some data of dengue fever disease. Ultimately, we debate the advantages and disadvantages of the followed approach and make a plan for some upcoming work.

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Bitopological approximation space with application to data reduction in multi-valued information systems

Cited by 18 publications

References 22 publications

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Topological approach to generate new rough set models

Rough sets models inspired by supra-topology structures

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