Efficient attribute reduction from the viewpoint of discernibility

Teng, Shenghua; Lü, Min; Yang, Afeng; Zhang, Jun; Nian, Yongjian; He, Mi

doi:10.1016/j.ins.2015.07.052

Cited by 39 publications

(11 citation statements)

References 31 publications

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“…Copyright ⓒ 2016 SERSC object pair and relative discernibility degree which is similar to the relative discernibility degree [19]. However, our uncertainty measure will generate less relative discernibility object pair and the computation is more feasible in parallel [17].…”

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confidence: 92%

Exploring the Boundary Region for Attribute Reduction in Inconsistent Decision Tables

Chang¹,

Jin²

2016

IJDTA

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confidence: 92%

Exploring the Boundary Region for Attribute Reduction in Inconsistent Decision Tables

Chang¹,

Jin²

2016

IJDTA

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“…In spite of the existence of a rich literature, attribute reduction is still a hot topic; a new area of its application and new methods of its improvement are constantly discovered (e.g. [3,5,14,15,22,25,39]).…”

Section: Introductionmentioning

confidence: 99%

Breadth search strategies for finding minimal reducts: towards hardware implementation

Choromański

Grzes

Hońko

2020

Neural Comput & Applic

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Attribute reduction, being a complex problem in data mining, has attracted many researchers. The importance of this issue rises due to ever-growing data to be mined. Together with data growth, a need for speeding up computations increases. The contribution of this paper is twofold: (1) investigation of breadth search strategies for finding minimal reducts in order to emerge the most promising method for processing large data sets; (2) development and implementation of the first hardware approach to finding minimal reducts in order to speed up time-consuming computations. Experimental research showed that for software implementation blind breadth search strategy is in general faster than frequency-based breadth search strategy not only in finding all minimal reducts but also in finding one of them. An inverse situation was observed for hardware implementation. In the future work, the implemented tool is to be used as a fundamental module in a system to be built for processing large data sets.Keywords Attribute reduction Á Minimal reducts Á Breadth search strategy Á Hardware implementation Á Field programmable gate arrays Neural Computing and Applications https://doi.org/10.1007/s00521-020-04833-7( 0123456789().,-volV) (0123456789(). ,-volV)Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Neural Computing and Applications

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“…As we can see, many existing methods of attribute reduction in neighborhood rough sets usually only start from the algebraic point of view or the information point of view, while the definition of attribute significance based on algebraic view only describes the effect of attributes on the subset of classification contained [ 46 ]. The definition of attribute significance based on information view only describes the influence of attributes on the uncertain classification subset contained in the domain and suitable for small-scale data sets [ 47 ]. Thus, they each have certain limitations in the real-world application.…”

Section: Introductionmentioning

confidence: 99%

A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

Sun

Wang

et al. 2019

Entropy

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For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy and high cardinality. In this paper, a novel attribute reduction method using Lebesgue and entropy measures in neighborhood rough sets is proposed, which has the ability of dealing with continuous numerical data whilst maintaining the original classification information. First, Fisher score method is employed to eliminate irrelevant attributes to significantly reduce computation complexity for high-dimensional data sets. Then, Lebesgue measure is introduced into neighborhood rough sets to investigate uncertainty measure. In order to analyze the uncertainty and noisy of neighborhood decision systems well, based on Lebesgue and entropy measures, some neighborhood entropy-based uncertainty measures are presented, and by combining algebra view with information view in neighborhood rough sets, a neighborhood roughness joint entropy is developed in neighborhood decision systems. Moreover, some of their properties are derived and the relationships are established, which help to understand the essence of knowledge and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is designed to improve the classification performance of large-scale complex data. The experimental results under an instance and several public data sets show that the proposed method is very effective for selecting the most relevant attributes with high classification accuracy.

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Efficient attribute reduction from the viewpoint of discernibility

Cited by 39 publications

References 31 publications

Exploring the Boundary Region for Attribute Reduction in Inconsistent Decision Tables

Exploring the Boundary Region for Attribute Reduction in Inconsistent Decision Tables

Breadth search strategies for finding minimal reducts: towards hardware implementation

A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

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