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

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 297
Author(s):  
Jining Zhou ◽  
Bo Zhang ◽  
Runhua Tan ◽  
Ming-Lang Tseng ◽  
Remen Chun-Wei Lin ◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Care Service ◽  
Elderly Care ◽  
Decisive Role ◽  
The Elderly ◽  
Traditional Concept ◽  
Neighborhood Rough Set ◽  
Multi Level

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.

Intuitionistic Fuzzy Neighborhood Rough Set Model for Feature Selection

2018 ◽  
Vol 7 (2) ◽  
pp. 75-84 ◽  
Author(s):  
Shivam Shreevastava ◽  
Anoop Kumar Tiwari ◽  
Tanmoy Som
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Continuous Data ◽  
Feature Subset ◽  
Data Set ◽  
Intuitionistic Fuzzy ◽  
Neighborhood Models ◽  
Neighborhood Rough Set

Feature selection is one of the widely used pre-processing techniques to deal with large data sets. In this context, rough set theory has been successfully implemented for feature selection of discrete data set but in case of continuous data set it requires discretization, which may cause information loss. Fuzzy rough set theory approaches have also been used successfully to resolve this issue as it can handle continuous data directly. Moreover, almost all feature selection techniques are used to handle homogeneous data set. In this article, the center of attraction is on heterogeneous feature subset reduction. A novel intuitionistic fuzzy neighborhood models have been proposed by combining intuitionistic fuzzy sets and neighborhood rough set models by taking an appropriate pair of lower and upper approximations and generalize it for feature selection, supported with theory and its validation. An appropriate algorithm along with application to a data set has been added.

scholarly journals A Novel Boundary Oversampling Algorithm Based on Neighborhood Rough Set Model: NRSBoundary-SMOTE

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Feng Hu ◽  
Hang Li
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Boundary Region ◽  
Mathematical Tool ◽  
Decision Space ◽  
Minority Class ◽  
Original Dataset ◽  
Neighborhood Rough Set ◽  
Powerful Mathematical Tool

Rough set theory is a powerful mathematical tool introduced by Pawlak to deal with imprecise, uncertain, and vague information. The Neighborhood-Based Rough Set Model expands the rough set theory; it could divide the dataset into three parts. And the boundary region indicates that the majority class samples and the minority class samples are overlapped. On the basis of what we know about the distribution of original dataset, we only oversample the minority class samples, which are overlapped with the majority class samples, in the boundary region. So, the NRSBoundary-SMOTE can expand the decision space for the minority class; meanwhile, it will shrink the decision space for the majority class. After conducting an experiment on four kinds of classifiers, NRSBoundary-SMOTE has higher accuracy than other methods when C4.5, CART, and KNN are used but it is worse than SMOTE on classifier SVM.

scholarly journals Research on full cycle evaluation of power grid projects based on rough set-multilevel extension theory

2021 ◽  
Vol 292 ◽  
pp. 01016
Author(s):  
Yuheng Sha ◽  
Jun Yan ◽  
Yanlai Zhang ◽  
Wenlong Li ◽  
Wenqi Li ◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Theoretical Foundation ◽  
Power Grid ◽  
Extension Theory ◽  
Evaluation Index System ◽  
New Ideas ◽  
Multi Level ◽  
Index Weight

Project full-cycle evaluation is the process of evaluating the level of project implementation. The evaluation has a clear purpose orientation and involves the entire cycle of the project. This paper proposes a research method for the full-cycle evaluation of power grid projects based on rough set-multi-level extension theory. First, this article combines various aspects of the project to establish a project evaluation index system involving multiple interests such as the environment and society. Then, the index weight is determined based on the rough set theory, and the multi-level extension theory is used to evaluate the project in the whole cycle. This model provides new ideas for the accurate evaluation of various types of projects and lays a theoretical foundation for improving the level of project management.

scholarly journals EVALUATING COMPLEX DECISION AND PREDICTIVE ENVIRONMENTS: THE CASE OF GREEN SUPPLY CHAIN FLEXIBILITY

2018 ◽  
Vol 24 (4) ◽  
pp. 1630-1658 ◽  
Author(s):  
Chunguang Bai ◽  
Joseph Sarkis
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Green Supply Chain ◽  
Chain Flexibility ◽  
Supply Chain Flexibility ◽  
Neighborhood Rough Set ◽  
Green Supply Chains

Supply chain flexibility is an important operations strategy dimension for organizations to achieve and maintain competitive advantage. With rising greener customer expectations and increasingly stringent environmental regulations, green supply chains are now viewed as another competitive weapon. Green supply chains are characterized by higher complexity and turbulence. Green supply chain flexibility can aid organizations function in this complex and uncertain environment, yet investigation into this area is very limited. This paper aims contribute to this field by investigating green supply chain flexibility achievement through information systems. This paper introduces a green supply chain flexibility matrix framework. Given the large data needs, as described in the matrix, a novel probability evaluation methodology that can help predict rankings of projects and programs is introduced. The methodology extends a TOPSIS based three-parameter interval grey number (TpGN) approach by incorporating neighborhood rough set theory (RST) to evaluate IS programs’ green flexibility support capability. The results of this methodology are more objective and effective for two reasons. (1) The results are predictive rankings based on probability degree instead of the fixed deterministic ranks. (2) Neighborhood rough set theory used in this study can limit loss of information when compared to rough set theory, yet still simplify extensive data sets. This paper also identifies study limitations and future research directions for green supply chain flexibility.

Hyperspectral band selection based on consistency-measure of neighborhood rough set theory

2016 ◽  
Vol 27 (5) ◽  
pp. 055501 ◽  
Author(s):  
Yao Liu ◽  
Hong Xie ◽  
Kezhu Tan ◽  
Yuehua Chen ◽  
Zhen Xu ◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Band Selection ◽  
Neighborhood Rough Set ◽  
Consistency Measure

Fuzzy integrated rough set theory situation feature extraction of network security

2021 ◽  
Vol 40 (4) ◽  
pp. 8439-8450
Author(s):  
Dongmei Zhao ◽  
Huiqian Song ◽  
Hong Li
Keyword(s):  
Network Security ◽  
Parallel Algorithm ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Conditional Entropy ◽  
Recall Rate ◽  
Mixed Data ◽  
Rank Matrix ◽  
Neighborhood Rough Set

The element extraction from network security condition is the foundation security awareness. Its excellence directly disturbsentire security system performance. In this paper we introduce fuzzy logic based rough set theory for extracting security conditional factors. The traditional extraction method of network security situation elements relies on a lot of prior knowledge. With the purpose of solving this issue, in this paper we proposed fuzzy rough set theory based featurerank matrix of neighborhood rough set. Additionally, we propose reduction based parallel algorithm that uses the concept of conditional entropy in order to constructs the feature rank matrix as well as, constructs the core attribute by using reduction rules, takes the threshold of standard deviation as the threshold, and redefines the multi threshold neighborhood of mixed data. The attack type recognition training is carried out on lib SVM, filtered classifier, j48 and random tree classifiers respectively. The results demonstrate that the proposed reduction based parallel algorithm can increase the accuracy of classification, shorten the modeling time, and show increased recall rate and decreased false alarm rate.

Impact of class noise on performance of hyperspectral band selection based on neighborhood rough set theory

2019 ◽  
Vol 188 ◽  
pp. 37-45
Author(s):  
Yao Liu ◽  
Xiaoda Cao ◽  
Xiangli Meng ◽  
Tao Wu ◽  
Xiaozhen Yan ◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Band Selection ◽  
Class Noise ◽  
Neighborhood Rough Set

scholarly journals Stable attribute reduction for neighborhood rough set

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1809-1815
Author(s):  
Shaochen Liang ◽  
Xibei Yang ◽  
Xiangjian Chen ◽  
Jingzheng Li
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Heuristic Algorithm ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Reduction Process ◽  
Neighborhood Rough Set ◽  
The Stability ◽  
Voting Strategy ◽  
The Given

In neighborhood rough set theory, traditional heuristic algorithm for computing reducts does not take the stability of the selected attributes into account, it follows that the performances of the reducts may not be good enough if the perturbations of data occur. To fill the gap, the mechanism of acquiring the most significant attribute is realized by two steps in the reduction process: firstly, several important attributes are derived in each iteration based on several radii which are close to the given radius for computing reduct; secondly, the most significant attribute is selected from them by a voting strategy. The experiments verify that such method can effectively improve the stabilities of the reducts, and it does not require too much attributes for constructing the reducts.

Sign in / Sign up

Export Citation Format

Share Document