Towards a Classification of Rough Set Bireducts

Benítez-Caballero, M. José; Medina, Jesús; Ramírez‐Poussa, Eloísa

doi:10.1007/978-3-030-50153-2_56

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, two particular cases of information bireducts are introduced. The first one was already proved in Benítez‐Caballero et al, 26 and it relates the notion of reduct to the notion of information bireduct.…”

Section: Bireducts Properties and The Relationship With Reductsmentioning

confidence: 93%

See 1 more Smart Citation

Value reducts and bireducts: A comparative study

Chacón-Gómez

Cornejo

Medina

et al. 2022

Math Methods in App Sciences

Self Cite

View full text Add to dashboard Cite

In Rough Set Theory, the notion of bireduct allows to simultaneously reduce the sets of objects and attributes contained in a dataset. In addition, value reducts are used to remove some unnecessary values of certain attributes for a specific object. Therefore, the combination of both notions provides a higher reduction of unnecessary data. This paper is focused on the study of bireducts and value reducts of information and decision tables. We present theoretical results capturing different aspects about the relationship between bireducts and reducts, offering new insights at a conceptual level. We also analyze the relationship between bireducts and value reducts. The studied connections among these notions provide important profits for the efficient information analysis, as well as for the detection of unnecessary or redundant information.

show abstract

Section: Bireducts Properties and The Relationship With Reductsmentioning

confidence: 93%

“…The last property shows that the information tables considered in Benítez‐Caballero et al 26 are actually information tables reduced by classes.…”

Section: Bireducts Properties and The Relationship With Reductsmentioning

confidence: 93%

Value reducts and bireducts: A comparative study

Chacón-Gómez

Cornejo

Medina

et al. 2022

Math Methods in App Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…A subset of individuals contemplated the selection of both examples and qualities in tandem. Furthermore, a significant proportion of previous research failed to consider the implications of extensive and unreliable data to effectively address the challenge of selecting both features and instances in massive data 12 . In following studies, researchers have employed feature selection techniques 13 based on genetic algorithms (GA) to address a range of difficulties.…”

mentioning

confidence: 99%

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

Saini,

Tiwari,

Nath

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

One-sided Concept Lattices by Blocks

Benítez-Caballero

Medina²

2022

Studies in Computational Intelligence

View full text Add to dashboard Cite

Towards a Classification of Rough Set Bireducts

Cited by 4 publications

References 11 publications

Value reducts and bireducts: A comparative study

Value reducts and bireducts: A comparative study

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

One-sided Concept Lattices by Blocks

Contact Info

Product

Resources

About