Local and Global Measurements of MGRS Rules

Yang, Xibei; Zhang, Yanqin; Yang, Jingyu

doi:10.1080/18756891.2012.747655

Cited by 18 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that a formal context, a special information system with two-valued input data 47 , can easily be represented by a two-dimensional table in which the rows are headed by the object names and the columns are headed by the attribute names. Definition 2.…”

Section: Preliminariesmentioning

confidence: 99%

On inference rules in decision formal contexts

Li¹,

Mei²,

Wang³

et al. 2015

IJCIS

View full text Add to dashboard Cite

Rule acquisition is one of the main purposes in the analysis of decision formal contexts. In general, the number of implications in a decision formal context is an exponential increase to the scale of the database. So, it is important to introduce effective inference rules between implications for eliminating as many superfluous implications as possible. This study puts forward a criterion called 'strongness' to assess the effectiveness of inference rules in terms of eliminating superfluous implications. We define a new inference rule in decision formal contexts and prove that the proposed inference rule is stronger than the existing one. Furthermore, we figure out the exact number of the superfluous implications that we can additionally remove by using the proposed inference rule compared with the existing one.

show abstract

Section: Preliminariesmentioning

confidence: 99%

On inference rules in decision formal contexts

Li¹,

Mei²,

Wang³

et al. 2015

IJCIS

View full text Add to dashboard Cite

show abstract

“…Though reviewing various modelling strategies in terms of multigranulation, it is not difficult to point out that Multigranulation Rough Set (MGRS) [16,61,62] aims to approximate the target through using multiple different results of information granulation [37,57]. Up to now, though a lot of MGRS have been proposed and carefully studied, most of the used information granulations only come from the homogeneous mechanism.…”

Section: Introductionmentioning

confidence: 99%

Triple-G: a new MGRS and attribute reduction

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Presently, with respect to different requirements, multigranulation rough set progressing rapidly 2,18,41,42,45,48,49,50,51,55 . We may classify the existing multigranulation rough sets into two categories.…”

Section: Introductionmentioning

confidence: 99%

Multigranulation rough set: A multiset based strategy

Yang¹,

Xu²,

Dou³

et al. 2017

IJCIS

Self Cite

View full text Add to dashboard Cite

A simple multigranulation rough set approach is to approximate the target through a family of binary relations. Optimistic and pessimistic multigranulation rough sets are two typical examples of such approach. However, these two multigranulation rough sets do not take frequencies of occurrences of containments or intersections into account. To solve such problem, by the motivation of the multiset, the model of the multiple multigranulation rough set is proposed, in which both lower and upper approximations are multisets. Such two multisets are useful when counting frequencies of occurrences such that objects belong to lower or upper approximations with a family of binary relations. Furthermore, not only the concept of approximate distribution reduct is introduced into multiple multigranulation rough set, but also a heuristic algorithm is presented for computing reduct. Finally, multiple multigranulation rough set approach is tested on eight UCI (University of California-Irvine) data sets. Experimental results show: 1. the approximate quality based on multiple multigranulation rough set is between approximate qualities based on optimistic and pessimistic multigranulation rough sets; 2. by comparing with optimistic and pessimistic multigranulation rough sets, multiple multigranulation rough set needs more attributes to form a reduct.

show abstract

Local and Global Measurements of MGRS Rules

Cited by 18 publications

References 24 publications

On inference rules in decision formal contexts

On inference rules in decision formal contexts

Triple-G: a new MGRS and attribute reduction

Multigranulation rough set: A multiset based strategy

Contact Info

Product

Resources

About