[1]LI Jingzheng,YANG Xibei,DOU Huili,et al.Research on ensemble significance based attribute reduction approach[J].CAAI Transactions on Intelligent Systems,2018,13(3):414-421.[doi:10.11992/tis.201706080]
Copy

Research on ensemble significance based attribute reduction approach

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 3 Page number: 414-421 Column: 学术论文—人工智能基础 Public date: 2018-05-05
Title:
Research on ensemble significance based attribute reduction approach
Author(s):
LI Jingzheng1;  YANG Xibei1; 2;  DOU Huili1;  WANG Pingxin3;  CHEN Xiangjian1
1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China;
2. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China;
3. School of Mathematics and Physics, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Keywords:
attribute reduction; classification; clustering; data perturbation; ensemble; heuristic algorithm; neighborhood rough set; stability
CLC:
TP391
DOI:
10.11992/tis.201706080
Abstract:
In the process of computing reduct using a heuristic algorithm, the attribute with the highest importance is gradually added in. However, this approach neglects the fluctuation of important calculations which is directly caused by data perturbation. Notably, such fluctuation may lead to an unstable reduct result. To eliminate such an anomaly, a framework consisting of a heuristic algorithm based on the importance of the ensemble attribute was proposed. In this approach, firstly, multiple sampling is executed for raw data; secondly, in each cycle, the importance of each attribute is computed on the basis of each sampling and the importance indices are integrated; finally, the attribute with the highest importance is added into the reduct. The experimental results obtained by utilizing the neighborhood rough set method show that the new approach not only obtains a more stable reduct, but also attains the classification results with high uniformity.
References:
[1] PAWLAK Z. Rough sets:theoretical aspects of reasoning about data[M]. Boston, Mass, USA:Kluwer Academic Publishers, 1991.
[2] PAWLAK Z. Rough sets[J]. International journal of computer & information sciences, 1982, 11(5):341-356.
[3] JU Hengrong, LI Huaxiong, YANG Xibei, et al. Cost-sensitive rough set:a multi-granulation approach[J]. Knowledge-based systems, 2017, 123:137-153, doi:10.1016/j.knosys.2017.02.019.
[4] XU Suping, YANG Xibei, YU Hualong, et al. Multi-label learning with label-specific feature reduction[J]. Knowledge-based systems, 2016, 104:52-61.
[5] DUBOIS D, PRADE H. Rough fuzzy sets and fuzzy rough sets[J]. International journal of general systems, 1990, 17(2/3):191-209.
[6] 胡清华, 于达仁, 谢宗霞. 基于邻域粒化和粗糙逼近的数值属性约简[J]. 软件学报, 2008, 19(3):640-649. HU Qinghua, YU Daren, XIE Zongxia. Numerical attribute reduction based on neighborhood granulation and rough approximation[J]. Journal of software, 2008, 19(3):640-649.
[7] YANG Xibei, CHEN Zehua, DOU Huili, et al. Neighborhood system based rough set:models and attribute reductions[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2012, 20(3):399-419.
[8] LIANG Jiye, WANG Feng, DANG Chuangyin, et al. An efficient rough feature selection algorithm with a multi-granulation view[J]. International journal of approximate reasoning, 2012, 53(6):912-926.
[9] 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, 56:1-15.
[10] JU Hengrong, YANG Xibei, YU Hualong, et al. Cost-sensitive rough set approach[J]. Information sciences, 2016, 355-356:282-298.
[11] JIA Xiuyi, LIAO Wenhe, TANG Zhenmin, et al. Minimum cost attribute reduction in decision-theoretic rough set models[J]. Information sciences, 2013, 219:151-167.
[12] 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.
[13] MIN Fan, HE Huaping, QIAN Yuhua, et al. Test-cost-sensitive attribute reduction[J]. Information sciences, 2011, 181(22):4928-4942.
[14] SONG Jingjing, TSANG E C C, CHEN Degang, et al. Minimal decision cost reduct in fuzzy decision-theoretic rough set model[J]. Knowledge-based systems, 2017, 126:104-112, doi:10.1016/j.knosys.2017.03.013.
[15] 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.
[16] 杨习贝, 颜旭, 徐苏平, 等. 基于样本选择的启发式属性约简方法研究[J]. 计算机科学, 2016, 43(1):40-43. YANG Xibei, YAN Xu, XU Suping, et al. New heuristic attribute reduction algorithm based on sample selection[J]. Computer science, 2016, 43(1):40-43.
[17] LI Yun, SI J, ZHOU Guojing, et al. FREL:a stable feature selection algorithm[J]. IEEE transactions on neural networks and learning systems, 2014, 26(7):1388-1402.
[18] 周林, 平西建, 徐森, 等. 基于谱聚类的聚类集成算法[J]. 自动化学报, 2012, 38(8):1335-1342. ZHOU Lin, PING Xijian, XU Sen, et al. Cluster ensemble based on spectral clustering[J]. Acta automatica sinica, 2012, 38(8):1335-1342.
[19] YANG Xibei, ZHANG Ming, DOU Huili, et al. Neighborhood systems-based rough sets in incomplete information system[J]. Knowledge-based systems, 2011, 24(6):858-867.
[20] QIAN Yuhua, WANG Qi, CHENG Honghong, et al. Fuzzy-rough feature selection accelerator[J]. Fuzzy sets and systems, 2014, 258:61-78.
[21] LI Jingzheng, YANG Xibei, SONG Xiaoning, et al. Neighborhood attribute reduction:a multi-criterion approach[J]. International journal of machine learning and cybernetics, 2017, doi:10.1007/s13042-017-0758-5.
[22] DEM?AR J. Statistical comparisons of classifiers over multiple data sets[J]. Journal of machine learning research, 2006, 7:1-30.
Similar References:

Memo

-

Last Update: 2018-06-25

Copyright © CAAI Transactions on Intelligent Systems