|Table of Contents|

Local attribute reduction approach based on decision-theoretic rough set


Research Field:
Publishing date:


Local attribute reduction approach based on decision-theoretic rough set
Wang Yu1Yang Zhirong1Yang Xibei12
1.School of Computer Science and Engineering,Jiangsu University of Science and Technology, Zhenjiang 212003,China; 2.School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China
attribute reduction cost heuristic algorithm Local attribute reduction monotonic criterion positive domain rule decision-theoretic rough set
Compared with classical rough sets,the decision-theoretic rough set model takes cost into account,which brings new challenges for solving attribute reduction in rough set.Some attribute reduction methods of decision-theoretic rough set have been put forward.However,the standards of these methods are based on all decision classes.It is too stringent for some condition.To solve this problem,from a local perspective,the idea Local attribute reduction is proposed.The experimental results based on the heuristic algorithm show that compared with the reduction based on all decision classes,Local attribute reduction can generate more positive domain rules and reduce the number of attributes.


[1] Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
Duan Jie,Hu Qinghua,Zhang Lingjun,et al.Feature selection for multi-label classification based on neighborhood rough sets[J].Journal of Computer Research and Development,2015,52(1):56-65.
[3]Yang Xibei,Song Xiaoning,Dou Huili,et al.Multi-granulation rough set:from crisp to fuzzy case[J].Annals Fuzzy Mathematics Information,2011,1(1):55-70.
[4]Hu Qinghua,Che Xunjian,Zhang Lei,et al.Rank entropy based decision trees for monotonic classification[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(11):2052-2064.
[5]Yao Yiyu,Wong S K M,Lingras P.A decision-theoretic rough set model[C]//Methodologies for Intelligent Systems.North-Holland,New York:[s.n],1990:17-24.
[6]Qian Yuhua,Zhang Hu,Sang Yanli,et al.Multigranula-tion decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[7]Yao Yiyu.Probabilistic rough set approximations[J].International Journal of Approximate Reasoning,2008,49(2):255-271.
[8]Li Huaxiong,Zhou Xianzhong,Huang Bing,et al.Cost-sensitive three-way decision:a sequential strategy[C]//Rough Sets and Knowledge Technology.Berlin Heidelberg:Springer,2013:325-337.
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]Liang Decui,Liu Dun,Pedrycz W,et al.Triangular fuzzy decision-theoretic rough sets[J].International Journal Approximate Reasoning,2013,54(8):1087-1106.
Yan Minlun.New rough set model:a variable precision multigranulation approach[J].Journal of Nanjing University of Science and Technology,2014,38(4):496-500.
[12]Liu Dun,Li Tianrui,Liang Decui.Incorporating logistic regression to decision-theoretic rough sets for classifications[J].International Journal of Approximate Reasoning,2014,55(1):197-210.
[13]Yu Hong,Liu Zhanguo,Wang Guoyin.An automatic method to determine the number of clusters using decision-theoretic rough set[J].International Journal of Approximate Reasoning,2014,55(1):101-115.
Jia Xiuyi,Li Weiwei,Shang Lin,et al.An adaptive learning parameters algorithm in three-way decision-theoretic rough set model[J].Acta Electronica Sinica,2011,39(11):2520-2525.
[15]贾修一,商琳.一种求三支决策阈值的模拟退火算法[J].小型微型计算机系统,2013,34(11):2603-2606. Jia Xiuyi,Shang Lin.A simulated annealing algorithm for learning thresholds in three-way decision-theoretic rough set model[J].Journal of Chinese Computer Systems,2013,34(11):2603-2606.
Wang Li,Zhou Xianzhong,Li Huaxiong.Fuzzy decision-throretic rough set model and its attribute reduction[J].Journal of Shanghai Jiao Tong University,2013,47(7):1032-1035.
Zhang Xianyong,A new type of classification region and relevant comparison analyses of the decision-theoretic rough set[J].Systems Engineering-Theory and Practice,2014,34(12):3204-3211.
[18]Dou Huili,Yang Xibei,Song Xiaoning,et al.Decision-theoretic rough set:a multicost strategy[J].Knowledge-Based Systems,2016,91:71-83.
[19]Zhao Yan,Yao Yiyu.Attribute reduction in decision-theoretic rough set models[J].Information Sciences,2008,178(17):3356-3373.
[20]Yang Xibei,Qi Yong,Yu Hualong,et al.Want More?Pay More![C]// Rough Set and Current Trends in Computing.Berlin Heidelberg:Springer,2014:144-151.
Ju Hengrong,Yang Xibei,Qi Yong,et al.Approach to monotonicity attribute reduction in quantitative rough set[J].Computer Science,2015,42(8):36-39.


Last Update: 2016-06-30