Order based hierarchies on hesitant fuzzy approximation space

Tsang, Eric W. K.; Jin, Zhen; Chen, Degang; Yang, Xibei

doi:10.1007/s13042-018-0822-9

Cited by 14 publications

(3 citation statements)

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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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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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“…Firstly, binary relation is effective to conduct information granulation [17][18][19][20][21]. In the classical rough set [22][23][24][25], binary relation is an equivalence relation, and each single equivalence class can be regarded as an information granule [26]. Nevertheless, the equivalence relation can only deal with categorical data, and this somewhat limits the applications of the rough set.…”

Section: Introductionmentioning

confidence: 99%

Combined Accelerator for Attribute Reduction: A Sample Perspective

Chen

Jin

Liu

et al. 2020

Mathematical Problems in Engineering

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In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

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“…FRS model is defined as an effective generalization of classical rough set model. This model is comprised of the advantages of both rough as well as fuzzy sets, which has been broadly and mainly applied to cope with the data reduction of real-valued datasets 36,37 . Jensen et al 38 initially employed this model to propose the idea of fuzzy rough aided dependency functions and presented an effective way to compute the reduct 39 .…”

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confidence: 99%

Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications

Saini,

Tiwari,

Nath

et al. 2024

Sci Rep

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The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

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Order based hierarchies on hesitant fuzzy approximation space

Cited by 14 publications

References 50 publications

Triple-G: a new MGRS and attribute reduction

Triple-G: a new MGRS and attribute reduction

Combined Accelerator for Attribute Reduction: A Sample Perspective

Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications

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