|Table of Contents|

Accelerator for finding reduct based on attribute group

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年02期
Page:
216-223
Research Field:
Publishing date:

Info

Title:
Accelerator for finding reduct based on attribute group
Author(s):
Chen Yan1; Song Jingjing1; 2; Yang Xibei1
1.School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.Key Laboratory of Data Science and Intelligent Application of Fujian Province University, Zhangzhou 363000,China
Keywords:
attribute groups;  attribute reduction;  bucket model;  neighborhood rough set
PACS:
TP181
DOI:
10.14177/j.cnki.32-1397n.2020.44.02.013
Abstract:
To improve the time efficiency of obtaining the reducts,based on the mechanism of bucket model,attributes are divided into different groups by considering the similarity between attributes. It follows that in the searching process of deriving reducts,attributes out of those groups containing at least one attribute in potential reducts should be evaluated,which can effectively reduce the searching space. Compared with the forward greedy searching approach,the experimental results show that the proposed strategy can significantly reduce the time consumption of obtaining reducts and the classification performance of reducts derived by using the strategy of attribute group is not decreased. This study provides a useful idea for accelerating the process of finding reducts.

References:

[1] Jia Xiuyi,Shang Lin,Zhou Bing,et al. Generalized attribute reduct in rough set theory[J]. Knowledge-Based Systems,2016,91:204-218.
[2]Yang Xibei,Qi Yunsong,Song Xiaoning,et al. Test cost sensitive multigranulation rough set:Model and minimal cost selection[J]. Information Sciences,2013,250:184-199.
[3]Chen Degang,Yang Yanyan,Dong Ze. An incremental algorithm for attribute reduction with variable precision rough sets[J]. Applied Soft Computing,2016,45:129-149.
[4]Jiang Zehua,Yang Xibei,Yu Hualong,et al. Accelerator for multi-granularity attribute reduction[J]. Knowledge-Based Systems,2019,177:145-158.
[5]Yang Xibei,Yao Yiyu. Ensemble selector for attribute reduction[J]. Applied Soft Computing,2018,70:1-11.
[6]Gao Yuan,Chen Xiangjian,Yang Xibei,et al. Neighborhood attribute reduction:A multicriterion strategy based on sample selection[J]. Information,2018,9(11):282-302.
[7]Min Fan,Zhu William. Attribute reduction of data with error ranges and test costs[J]. Information Sciences,2012,211(211):48-67.
[8]李京政,杨习贝,王平心,等. 模糊粗糙集的稳定约简算法[J]. 南京理工大学学报,2018,42(1):68-75.
Li Jingzheng,Yang Xibei,Wang Pingxin,et al. Stable attibute reduction approach for fuzzy rough set[J]. Journal of Nanjing University of Science and Technology,2018,42(1):68-73.
[9]杨习贝,杨静宇. 邻域系统粗糙集模型[J]. 南京理工大学学报,2012,36(2):291-295.
Yang Xibei,Yang Jingyu. Rough set model based on neighborhood system[J]. Journal of Nanjing University of Science and Technology,2012,36(2):291-295.
[10]Chen Hongmei,Li Tianrui,Luo Chuan,et al. A decision-theoretic rough set approach for dynamic data mining[J]. IEEE Transactions on Fuzzy Systems,2015,23(6):1958-1970.
[11]孟军,张晶晶,姜丁菱. 结合紧邻传播聚类的选择性集成分类方法[J]. 计算机研究与发展,2018,55(5):986-993.
Meng Jun,Zhang Jingjing,Jiang Dingling. Selective ensemble classification integrated with affinity propagation clustering[J]. Journal of Computer Research and Development,2018,55(5):986-993.
[12]Liu Yong,Huang Wenliang,Jiang Yunliang,et al. Quick attribute reduct algorithm for neighborhood rough set model[J]. Information Sciences,2014,271:65-81.
[13]Wang Changzhong,Hu Qinghua,Wang Xizhao,et al. Feature selection based on neighborhood discrimination index[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(7):2986-2999.
[14]Zhao Hong,Wang Ping,Hu Qinghua. Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence[J]. Information Sciences,2016,366:134-149.
[15]姜泽华,王怡博,徐刚,等. 面向多尺度的属性约简加速器[J]. 计算机科学,2019,46:250-256.
Jiang Zehua,Wang Yibo,Xu Gang,et al. Multi-scale based accelerator for attribute reduction[J]. Computer Science,2019,46:250-256.
[16]王熙照,王婷婷,翟俊海. 基于样例选取的属性约简算法[J]. 计算机研究与发展,2012,49(11):2305-2310.
Wang Xizhao,Wang Tingting,Zhai Junhai. An attribute reduction algorithm based on instance selection[J]. Journal of Computer Research and Development,2012,49(11):2305-2310.
[17]Chen Degang,Zhao Suyun. Local reduction of decision system with fuzzy roughsets[J]. Fuzzy Sets and Systems,2010,161(13):1871-1883.
[18]Zhang Xiao,Mei Changlin,Chen Degang,et al. Feature selection in mixed data:a method using a novel fuzzy rough set-based information entropy[J]. Pattern Recognition,2016,56(1):1-15.
[19]Hu Qinghua,Yu Daren,Xie Zongxia. Neighborhood classifiers[J]. Expert Systems with Applications,2008,34(2):866-876.
[20]Liu Keyu,Yang Xibei,Yu Hualong,et al. Rough set based semi-supervised feature selection via ensemble selector[J]. Knowledge-Based Systems,2019,165:282-296.
[21]张倚萌,贾修一,唐振民. 基于条件信息熵的区间集决策信息表不确定性度量[J]. 南京理工大学学报,2019,43(4):393-401.
Zhang Yimeng,Jia Xiuyi,Tang Zhenmin. Uncertainty measurement for interval set decision informationtables based on conditional information entropy[J]. Journal of Nanjing University of Science and Technology,2019,43(4):393-401.
[22]Jiang Zehua,Liu Keyu,Yang Xibei,et al. Accelerator for supervised neighborhood based attribute reduction[J]. International Journal of Approximate Reasoning,2020,119:122-150.
[23]Yang Xibei,Liang Shaochen,Yu Hualong,et al. Pseudo-label neighborhood rough set:Measures and attribute reductions[J]. International Journal of Approximate Reasoning,2019,105:115-129.
[24]Liu Keyu,Yang Xibei,Yu Hualong,et al. An efficient selector for multi-granularity attribute reduction[J]. Information Sciences,2019,505:457-472.
[25]李智远,杨习贝,徐苏平,等. 邻域决策一致性的属性约简方法研究[J]. 河南师范大学学报(自然科学版),2017(5):68-73.
Li Zhiyuan,Yang Xibei,Xu Suping,et al. Attribute reduction approach to neighborhood decision agreement[J]. Journal of Henan Normal University(Natural Science Edition),2017(5):68-73.
[26]Hu Qinghua,Yu Daren,Liu Jinfu,et.al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information Sciences,2008,178:3577-3594.
[27]Wang Rui,Li Wei,Li Rui,et al. Automatic blur type classification via ensemble SVM[J]. Signal Processing:Image Communication,2019,71:24-35.

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Last Update: 2020-04-20