Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Gao, Yan; Lv, Changwei; Wu, Zhengjiang

doi:10.2991/ijcis.d.200915.004

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Raza and Qamar [14] proposed a new heuristic-based dependency calculation method, which avoided the timeconsuming calculation of the positive region and helped increase the performance of subsequent algorithms. Gao et al [15] proposed a reduction algorithm (ARNI) for continuous value information systems, which could significantly speed up by graphics processing units. Chen et al [16] proposed an acceleration strategy based on attribute group, which reduced the number of evaluations of candidate attributes.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Attribute Reduction Algorithm for High-Dimensional Data under the Spark Framework

Mei

Zhang

et al. 2022

Int J Comput Intell Syst

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Attribute reduction is an important issue in rough set theory. However, the rough set theory-based attribute reduction algorithms need to be improved to deal with high-dimensional data. A distributed version of the attribute reduction algorithm is necessary to enable it to effectively handle big data. The partition of attribute space is an important research direction. In this paper, a distributed attribution reduction algorithm based on cosine similarity (DARCS) for high-dimensional data pre-processing under the Spark framework is proposed. First, to avoid the repeated calculation of similar attributes, the algorithm gathers similar attributes based on similarity measure to form multiple clusters. And then one attribute is selected randomly as a representative from each cluster to form a candidate attribute subset to participate in the subsequent reduction operation. At the same time, to improve computing efficiency, an improved method is introduced to calculate the attribute dependency in the divided sub-attribute space. Experiments on eight datasets show that, on the premise of avoiding critical information loss, the reduction ability and computing efficiency of DARCS have been improved by 0.32 to 39.61% and 31.32 to 93.79% respectively compared to the distributed version of attribute reduction algorithm based on a random partitioning of the attributes space.

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Section: Introductionmentioning

confidence: 99%

A Distributed Attribute Reduction Algorithm for High-Dimensional Data under the Spark Framework

Mei

Zhang

et al. 2022

Int J Comput Intell Syst

Self Cite

View full text Add to dashboard Cite

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“…Wang et al [9] gave some basic structural properties of attribute reductions in covering rough sets, and proposes a heuristic algorithm based on discernibility matrix to find the approximate minimum reduction attribute subsets. Gao et al [10] rewrote the matrix form of neighborhood approximation set. Based on the neighborhood approximation set of matrix, the relative dependence of attributes and its algorithm are proposed.…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction Method of Covering Rough Set Based on Dependence Degree

Li¹,

Ren²,

Jin³

2021

IJCIS

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Attribute reduction is a hot topic in the field of data mining. Compared with the traditional methods, the attribute reduction algorithm based on covering rough set is more suitable for dealing with numerical data. However, this kind of algorithm is still not efficient enough to deal with large-scale data. In this paper, we firstly propose -Boolean identification matrix of covering rough sets, give the calculation methods of dependence degree and local dependence degree, and further discuss their properties. Secondly, we give two attribute reduction algorithms based on dependence degree and local dependence degree, respectively. Finally, we test the performance of the algorithm through several UCI data sets. Experimental results show that the efficiency of our algorithm has been greatly improved. So it is more suitable for handling large-scale data processing problems, and can have wide application value.

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Three-way concept lattice based on Boolean formal context

Niu,

2024

International Journal of Approximate Reasoning

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Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Cited by 5 publications

References 34 publications

A Distributed Attribute Reduction Algorithm for High-Dimensional Data under the Spark Framework

A Distributed Attribute Reduction Algorithm for High-Dimensional Data under the Spark Framework

Attribute Reduction Method of Covering Rough Set Based on Dependence Degree

Three-way concept lattice based on Boolean formal context

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