Attribute reduction based on max-decision neighborhood rough set model

Fan, Xiaodong; Wenju, Zhao; Wang, Changzhong; Huang, Yang

doi:10.1016/j.knosys.2018.03.015

Cited by 67 publications

(24 citation statements)

References 54 publications

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“…The main purpose of attribute reduction is based on neighborhood rough set to eliminate redundant attributes classified data and extract useful information. Firms' business methods are changing from product-centric to (1) Singular value decomposition: This method has a good effect when the data dimension is high, and it is often used as a preprocessing method to realize the convergence of fuzzy rough reduction in high-dimensional data sets; but the calculation cost of this method is high [13,14]; (2) The principal component analysis is reduced, and the mutual influence between evaluation indexes is eliminated by replacing the original variables with several principal components with larger contributions. This study is necessary to delete irrelevant or unimportant attributes to eliminate the interference of irrelevant features when using the data with higher dimensions [15]; (3) Effective feature extraction in deep learning: Data-driven deep learning analysis has been developed and applied in many fields.…”

Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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“…Therefore, the time complexity of ARNRJE algorithm is approximately O ( mn ). So far, ARNRJE appears to be more efficient than some of the existing algorithms for attribute reduction in [ 18 , 36 , 45 , 52 , 53 ] for neighborhood decision systems. Furthermore, its space complexity is O ( mn ).…”

Section: Attribute Reduction Using Lebesgue and Entropy Measures Imentioning

confidence: 99%

“…Sun and Xu [ 35 ] proposed a positive region-based granular space for feature selection based on rough sets. Nevertheless, the positive region in these models only draws attention to the consistent samples whose similarity classes are completely contained in some decision classes [ 36 ]. Meng et al [ 37 ] presented an intersection neighborhood for numerical data, and designed a gene selection algorithm using positive region and gene ranking based on neighborhood rough sets.…”

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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“…An attribute reduction method [10] was proposed based on Max-Decision Neighborhood Rough Set model (MDNRS) for attribute reduction. This method focused on the boundary samples and enlarged the positive region by adding the samples whose neighborhoods have a maximal intersection with some decision classes.…”

Section: Literature Surveymentioning

confidence: 99%

A Parallel Rough Set Theory for Nonlinear Dimension-Reduction in Big Data Analysis

Muthusamy¹,

Subramani²

2019

IJIES

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Attribute reduction for big data is an important preprocessing step in the area of data mining. A multi-step dimension reduction approach was proposed for attribute reduction in big data. It addressed the non-linear relationships within the attributes. The data dimension was reduced through a parametric mapping. The mapping parameters were estimated using low-rank Singular Value Decomposition (SVD). However, the user-defined criterion in multi-step dimension reduction approach has greatly influenced the efficiency of attribute reduction. This approach was proposed for a single machine that means the entire big data must fit in the main memory and the parallelism was limited. So, in this paper, parallel rough set theory based attribute reduction approach is proposed for attribute reduction in big data. Based on two descriptions of lower approximation and upper approximation, a rough set is constructed. Then a reduct is detected using inner importance measure and outer importance measure. The rough set theory is used in MapReduce framework to achieve the parallelism for attribute reduction in big data. Hence, the computation time is reduced by using parallel rough set theory based attribute reduction approach. Finally, the experiments are carried out in Amazon customer review, REUTERS-21578 and International Cancer Genome Consortium (ICGC) on AWS datasets to prove the effectiveness of parallel rough set theory based attribute reduction in terms of accuracy, precision, recall and computation time.

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Attribute reduction based on max-decision neighborhood rough set model

Cited by 67 publications

References 54 publications

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

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

A Parallel Rough Set Theory for Nonlinear Dimension-Reduction in Big Data Analysis

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