Attribution reduction based on sequential three-way search of granularity

Wang, Xun; Wang, Pingxin; Yang, Xibei; Yao, Yiyu

doi:10.1007/s13042-020-01244-x

Cited by 32 publications

(5 citation statements)

References 64 publications

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“…Attribute reduction plays an important role on data mining for the problem on uncertain circumstance. In recent years, many scholars explored the approaches to study the attribute reduction (Zhang 2005;Wang et al 2021;Li et al 2017;Fan et al 2018). Neighborhood rough set (Hu et al 2008a) is proposed based on similarity degree.…”

Section: Neighborhood Rough Set-based Attribute Reductionmentioning

confidence: 99%

An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem

Sun

Tong

et al. 2021

Artif Intell Rev

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Exploring efficiency approaches to solve the problems of decision making under uncertainty is a mainstream direction. This article explores the rough approximation of the uncertainty information with Pythagorean fuzzy information on multi-granularity space over two universes combined with grey relational analysis. Based on grey relational analysis, we present a new approach to calculate the relative degree or the attribute weight with Pythagorean fuzzy set and give a new descriptions for membership degree and nonmembership. Then, this paper proposes a multi-granulation rough sets combined with Pythagorean fuzzy set, including optimistic multi-granulation Pythagorean fuzzy rough set, pessimistic multi-granulation Pythagorean fuzzy rough set and variable precision Pythagorean fuzzy rough set. Several basic properties for the established models are investigated in detail. Meanwhile, we present an approach to solving the multiple-criteria group decision making problems with fuzzy information based on the proposed model. Eventually, a case study of psychological evaluation of health care workers in COVID-19 show the principle of the established model and is utilized to verify the availability. The main contributions have three aspects. The first contribution of an approach of calculating the attribute weight is presented based on Grey Relational Analysis and gives a new perspective for the Pythagorean fuzzy set. Then, this paper proposes a mutli-granulation rough set model with Pythagorean fuzzy set over two universes. Finally, we apply the proposed model to solving the psychological evaluation problems.

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Section: Neighborhood Rough Set-based Attribute Reductionmentioning

confidence: 99%

An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem

Sun

Tong

et al. 2021

Artif Intell Rev

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“…In recent years, attribute reduction methods have been used in granular computing [25], [26] formal concept analysis [27], three-way decision analysis [28], [29] and many other fields. Attribute reduction in classical rough sets has been studied thoroughly.…”

Section: Introductionmentioning

confidence: 99%

New Variable Precision Reduction Algorithm for Decision Tables

Xiao

Tang

2023

IEEE Access

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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. INDEX TERMSVariable precision reduction, positive region reduction, decision table, compacted decision table, weighted decision table.

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“…1. On the one hand, the structure related topics are explored in view of the multiple different granulations [15,47,59]. For example, by examining the relationships among different information granulations, Yang et al [55] have systematically revealed the hierarchical structures of the multigranulation space; Qian et al [30] have proposed a serious of measures for characterizing the coarser or finer relationships over the multigranulation space, immediately, the order structure of the multigranulation space can be quantitatively represented; Lin et al [18] have investigated the uncertainty measures in multigranulation space based on the hierarchical structure.…”

Section: Introductionmentioning

confidence: 99%

Triple-G: a new MGRS and attribute reduction

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

Self Cite

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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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Attribution reduction based on sequential three-way search of granularity

Cited by 32 publications

References 64 publications

An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem

An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem

New Variable Precision Reduction Algorithm for Decision Tables

Triple-G: a new MGRS and attribute reduction

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