| 120 | 2 | 274 |
| 下载次数 | 被引频次 | 阅读次数 |
为了进一步改进邻域分类器的分类机制,提升分类性能,提出Dempster-Shafer(D-S)证据理论驱动的邻域粗糙分类方法。首先,采用邻域决策错误率作为属性重要性的指标研究基于邻域决策错误率的属性约简方法,通过删除冗余属性,为分类学习提供重要的属性集合;其次,改变传统多数投票机制,将D-S证据理论引入邻域样本的信息融合中,提出基于D-S证据理论的邻域分类器;最后,基于UCI公共数据集的实验结果表明,所提方法相较于多数投票机制下的邻域分类器,具有更高的分类精度,为邻域分类方法的进一步研究提供了新的思路。
Abstract：In order to further improve the classification mechanism and the performance of neighborhood classifier, a Dempster-Shafer(D-S) evidence-driven neighborhood rough classification method is proposed. Firstly, in attribute reduction, the error rate of neighborhood decision is used as the index of attribute significance, and the attribute reduction method based on neighborhood decision error rate is studied. By removing redundant attributes, an important set of attributes is provided for classification learning. Then, in terms of classifier design, the traditional majority voting mechanism is revised, D-S evidence theory is introduced into the information fusion of neighborhood samples,and a neighborhood classifier based on D-S evidence theory is proposed. Experimental results on UCI public data set show that the proposed method has higher classification accuracy than the neighborhood classifier under the majority voting mechanism. The paper provides a new insight for the further study of neighborhood classification methods.
[1]PAWLAK Z.Rough set theory and its applications to data analysis[J].Cybernetics and Systems,1998,29(7):661-688.
[2]PAWLAK Z.Rough sets and intelligent data analysis[J].Information Sciences,2002,147(1/2/3/4):1-12.
[3]YAO Y Y,WONG S K M.A decision theoretic framework for approximating concepts[J].International Journal of ManMachine Studies,1992,37(6):793-809.
[4]YAO Y Y.The superiority of three-way decisions in probabilistic rough set models[J].Information Sciences,2011,181(6):1080-1096.
[5]LIU J B,LI H X,ZHOU X Z,et al.An optimization-based formulation for three-way decisions[J].Information Sciences,2019,495:185-214.
[6]LIN T Y.Neighborhood systems and relational databases[C]//Proceedings of the 1988 ACM Sixteenth Annual Conference on Computer Science-CSC′88,Atlanta,Georgia,USA.New York:ACM Press,1988:725-726.
[7]HU Q H,YU D R,LIU J F,et al.Neighborhood rough set based heterogeneous feature subset selection[J].Information Sciences,2008,178(18):3577-3594.
[8]YANG X B,LIANG S C,YU H L,et al.Pseudo-label neighborhood rough set:measures and attribute reductions[J].International Journal of Approximate Reasoning,2019,105:112-129.
[9]HU Q H,YU D R,XIE Z X.Neighborhood classifiers[J].Expert Systems with Applications,2008,34(2):866-876.
[10]LIU K Y,YANG X B,FUJITA H,et al.An efficient selector for multi-granularity attribute reduction[J].Information Sciences,2019,505:457-472.
[11]LIU K Y,YANG X B,YU H L,et al.Rough set based semi-supervised feature selection via ensemble selector[J].Knowledge-Based Systems,2019,165:282-296.
[12]XU S P,WANG P X,LI J H,et al.Attribute reduction:an ensemble strategy[C]//Proceedings of the 2017 International Joint Conference on Rough Sets,July 3-7,2017,Olsztyn,Poland.Cham:Springer International Publishing,2017:362-375.
[13]XU S P,YANG X B,TSANG E C C,et al.Neighborhood collaborative classifiers[C]//Proceedings of the 2016 International Conference on Machine Learning and Cybernetics,July 10-13,2016,Jeju,South Korea.New York:IEEEXplore,2016:470-476.
[14]BISWAS N,CHAKRABORTY S,MULLICK S S,et al.Aparameter independent fuzzy weighted k-Nearest neighbor classifier[J].Pattern Recognition Letters,2018,101:80-87.
[15]李峰，苗夺谦，张志飞，等.基于互信息的粒化特征加权多标签学习k近邻算法[J].计算机研究与发展，2017,54(5):1024-1035.
[16]DENOEUX T.A k-nearest neighbor classification rule based on Dempster-Shafer theory[J].IEEE Transactions on Systems,Man,and Cybernetics,1995,25(5):804-813.
[17]ZHANG Y Q,CAO G,WANG B S,et al.A novel ensemble method for k-nearest neighbor[J].Pattern Recognition,2019,85:13-25.
[18]HU Q H,PEDRYCZ W,YU D R,et al.Selecting discrete and continuous features based on neighborhood decision error minimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2010,40(1):137-150.
[19]余思成，杨习贝，陈向坚，等.邻域决策的随机约简与集成分类研究[J].小型微型计算机系统，2018,39(6):1163-1167.
[20]University of California,Irvine.UC Irvine Machine Learning Repository[DB/OL].[2019-08-20].http://archive.ics.uci.edu/ml/index.php.（责任编辑：仇慧）
基本信息:
DOI：10.12194/j.ntu.20190920001
中图分类号:TP18
引用信息:
[1]鞠恒荣,丁卫平,尹涛,等.Dempster-Shafer证据理论驱动的邻域粗糙分类方法[J],2021,20(01):28-33.DOI:10.12194/j.ntu.20190920001.
基金信息:
国家自然科学基金项目（62006128,61976120,62076111）;; 江苏省双创博士计划;; 江苏省高等学校自然科学研究项目（20KJB520009）;; 江苏省自然科学基金项目（BK20191445）;; 江苏省六大人才高峰项目（XYDXXJS-048）;; 江苏高校“青蓝工程”项目（苏教师[2019]3号）;; 南通市科技计划项目（JC2020141）