A neighborhood rough set model for attribute reduction without distance metric

2021 ◽  
Author(s):  
Xingxin Chen ◽  
Shuyin Xia ◽  
Feng Hu
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Distance Metric ◽  
Neighborhood Rough Set

scholarly journals Multi-Label Attribute Reduction Based on Variable Precision Fuzzy Neighborhood Rough Set

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 133565-133576
Author(s):  
Panpan Chen ◽  
Menglei Lin ◽  
Jinghua Liu
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Variable Precision ◽  
Neighborhood Rough Set

Attribute reduction based on max-decision neighborhood rough set model

2018 ◽  
Vol 151 ◽  
pp. 16-23 ◽  
Author(s):  
Xiaodong Fan ◽  
Weida Zhao ◽  
Changzhong Wang ◽  
Yang Huang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Neighborhood Rough Set

Combined Accelerator for Attribute Reduction: A Sample Perspective

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Chen ◽  
Jingjing Song ◽  
Keyu Liu ◽  
Yaojin Lin ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Data Sets ◽  
Multiple Views ◽  
Time Consumption ◽  
Elapsed Time ◽  
Rough Approximation ◽  
Neighborhood Rough Set ◽  
Speed Up ◽  
Neighborhood Relation

In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

Signals Modulation Recognition Based on Efficient Attribute Reduction with Neighborhood Rough Set

2012 ◽  
Vol 220-223 ◽  
pp. 2301-2307
Author(s):  
Ying Zheng Han ◽  
Juan Ping Wu ◽  
Xiao Fang Liang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Recognition Rate ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signals ◽  
Time Frequency ◽  
Feature Parameters ◽  
Neighborhood Rough Set ◽  
Propagation Network

The purpose of communication signals automatic modulation recognition is to judge signal modulation styles and estimate signal modulation parameters on the precondition of unknown modulation information. According to the seven kinds communication modulation signals studied in this paper, select a group of feature parameters based on the time-frequency characteristics of communication signals. The fast algorithm for attribute reduction based on neighborhood rough set using feature selection is introduced in detail. Then, using back propagation network as classification instruments to identify signals. The simulation shows that the method can not only reduce the number of feature parameters, but also improve the recognition rate.

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.

scholarly journals Parallel attribute reduction in dominance-based neighborhood rough set

2016 ◽  
Vol 373 ◽  
pp. 351-368 ◽  
Author(s):  
Hongmei Chen ◽  
Tianrui Li ◽  
Yong Cai ◽  
Chuan Luo ◽  
Hamido Fujita
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Neighborhood Rough Set

scholarly journals Attribute Reduction Algorithm of Neighborhood Rough Set Based on Supervised Granulation and Its Application

2022 ◽  
Author(s):  
li zou ◽  
Siyuan Ren ◽  
Yibo Sun ◽  
Xinhua Yang
Keyword(s):  
Titanium Alloy ◽  
Fatigue Life ◽  
Influencing Factors ◽  
Rough Set ◽  
Mutual Influence ◽  
Attribute Reduction ◽  
Reduction Rate ◽  
Reduction Algorithm ◽  
Relationship Analysis ◽  
Neighborhood Rough Set

Abstract In neighborhood rough set theory, attribute reduction based on measure of information has important application significance. The influence of different decision classes was not considered for calculation of traditional conditional neighborhood entropy, and the improvement of algorithm based on conditional neighborhood entropy mainly includes of introducing multi granularity and different levels, while the mutual influence between samples with different labels is less considered. To solve this problem, this paper uses the supervised strategy to improve the conditional neighborhood entropy of three-layer granulation. By using two different neighborhood radii to adjust the mutual influence degree of different label samples, and by considering the mutual influence between conditional attributes through the feature complementary relationship, a neighborhood rough set attribute reduction algorithm based on supervised granulation is proposed. Experiment results on UCI data sets show that the proposed algorithm is superior to the traditional conditional neighborhood entropy algorithm in both aspects of reduction rate and reduction accuracy. Finally, the proposed algorithm is applied to the evaluation of fatigue life influencing factors of titanium alloy welded joints. The results of coupling relationship analysis show that the effect of joint type should be most seriously considered in the calculation of stress concentration factor. The results of influencing factors analysis show that the stress range has the highest weight among all the fatigue life influencing factors of titanium alloy welded joint.

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