一种基于证据理论的多类半监督分类算法

doi:10.3969/j.issn.0372-2112.2018.11.011

电子学报 ›› 2018, Vol. 46 ›› Issue (11): 2642-2649.DOI: 10.3969/j.issn.0372-2112.2018.11.011

一种基于证据理论的多类半监督分类算法

盛凯¹, 刘忠¹, 周德超¹, 魏启航², 冯成旭¹

1. 海军工程大学兵器工程学院, 湖北武汉 430033;
2. 66029部队, 内蒙古锡林郭勒盟 011216

收稿日期:2017-12-03 修回日期:2018-04-22 出版日期:2018-11-25
- 作者简介:
- 盛凯男,1991年出生,山东兰陵人,海军工程大学兵器工程学院博士研究生.主要研究方向为轨迹数据挖掘、机器学习及复杂系统建模与仿真.E-mail:shengkai0214@foxmail.com;刘忠男,1963年出生,山东龙口人,海军工程大学兵器工程学院教授、博士生导师.主要研究方向为系统工程、复杂系统建模与仿真、系统集成技术.E-mail:liuzhong531@yahoo.cn;周德超 男,1972年出生,山东荣成人,海军工程大学兵器工程学院副教授,硕士生导师.主要研究方向为复杂系统建模与仿真、数据挖掘、人工智能.E-mail:13397190531@189.cn
- 基金资助:
- 湖北省自然科学基金 (No.2017CFB377）

A Multi-class Semi-Supervised Classification Algorithm Based on Evidence Theory

SHENG Kai¹, LIU Zhong¹, ZHOU De-chao¹, WEI Qi-hang², FENG Cheng-xu¹

1.College of Weapons Engineering, Naval University of Engineering, Wuhan, Hubei 430033, China;
2.PLA 66029 Troop, Xilinguole, Inner-Mongolia 011216, China

Received:2017-12-03 Revised:2018-04-22 Online:2018-11-25 Published:2018-11-25
- Supported by:
- Natural Science Foundation of Hubei Province, China (No.2017CFB377)

摘要/Abstract

摘要： 为了提高多类半监督分类的性能，提出了一种基于证据理论的多类协同森林算法（DSM-Co-Forest）.首先，通过"多对多"模式将有标记的多类数据随机拆分为多个二类数据集，并以此训练二类基分类器；然后，利用多个基分类器同时对未标记样本进行预测，并利用证据组合算法挑选出可信度较高的未标记样本；最后，将高可信度的未标记样本加入到原训练样本中，以迭代更新其他的基分类器，从而提高分类器的整体性能.通过在一些公共数据集上进行实验，并与其他半监督分类算法进行对比，验证了所提算法的可行性和有效性.

关键词: 半监督学习, 多类分类, 证据理论, 协同森林

Abstract: In order to improve the performance of multi-class semi-supervised classification, a new multi-class Co-Forest algorithm named DSM-Co-Forest is proposed on the basis of D-S evidence theory. First, through MVM mode, the multi-labeled data set is randomly split into multiple binary-class data set to train the base classifiers; then, these base classifiers are used to pick out the high reliability samples from the unlabeled data set by using the evidence combination algorithm; finally, adds these selected samples to the original training set to iteratively update the base classifiers so as to improve the overall performance of the multi-class classifier. Through comparing with other semi-supervised classification algorithms on several public data sets, the feasibility and validity of the proposed algorithm are verified.

Key words: semi-supervised learning, multi-class classification, evidence theory, co-forest

中图分类号:

TP391

盛凯, 刘忠, 周德超, 等. 一种基于证据理论的多类半监督分类算法[J]. 电子学报, 2018, 46(11): 2642-2649.

SHENG Kai, LIU Zhong, ZHOU De-chao, et al. A Multi-class Semi-Supervised Classification Algorithm Based on Evidence Theory[J]. Acta Electronica Sinica, 2018, 46(11): 2642-2649.

参考文献

[1] 刘建伟,刘媛,罗雄麟.半监督学习方法[J].计算机学报,2015,38(8):1592-1617. LIU Jian-wei,LIU Yuan,LUO Xiong-lin.Semi-supervised learning methods[J].Chinese Journal of Computers,2015,38(8):1592-1617.(in Chinese)
[2] 周志华.基于分歧的半监督学习[J].自动化学报,2013,39(11):1871-1878. ZHOU Zhi-hua.Disagreement-based semi-supervised learning[J].Acta Automatic Sinica,2013,39(11):1871-1878.(in Chinese)
[3] 蔡毅,朱秀芳,孙章丽,等.半监督集成学习综述[J].计算机科学,2017,44(s1):7-13. CAI Yi,ZHU Xiu-fang,SUN Zhang-li,et al.Semi-supervised and ensemble learning:a review[J].Computer Science,2017,44(s1):7-13.(in Chinese)
[4] BLUM A,MITCHELL T.Combining labeled and unlabeled data with co-training[A].Eleventh Conference on Computational Learning Theory[C].New York:ACM Press,1998.92-100.
[5] ZHOU Z H,LI M.Tri-training:exploiting unlabeled data using three classifiers[J].IEEE Transactions on Knowledge & Data Engineering,2005,17(11):1529-1541.
[6] LI M,ZHOU Z H.Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples[J].IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2007,37(6):1088-1098.
[7] DENG C,GUO M Z.A new co-training-style random forest for computer aided diagnosis[J].Journal of Intelligent Information Systems,2011,36(3):253-281.
[8] 于重重,商利利,谭励,等.一种增强差异性的半监督协同分类算法[J].电子学报,2013,41(1):35-41. YU Chong-chong,SHANG Li-li,TAN Li,et al.A semi-supervised collaboration classification algorithm with enhanced difference[J].Acta Electronica Sinica,2013,41(1):35-41.(in Chinese)
[9] 陈思,苏松志,李绍滋,等.基于在线半监督boosting的协同训练目标跟踪算法[J].电子与信息学报,2014,36(4):888-895. CHEN Si,SU Song-zhi,LI Shao-zi,et al.A novel co-training object tracking algorithm based on online semi-supervised boosting[J].Journal of Electronics & Information Technology,2014,36(4):888-895.(in Chinese)
[10] 张磊,邵振峰,周熙然,等.聚类特征和SVM组合的高光谱影像半监督协同分类[J].测绘学报,2014,43(8):855-861. ZHANG Lei,SHAO Zhen-feng,ZHOU Xi-ran,et al.Semi-supervised collaboration classification for hyperspectral remote sensing image with combination of cluster feature and SVM[J].Acta Geodaetica et Cartographica Sinica,2014,43(8):855-861.(in Chinese)
[11] 邹保平,戚伟强.基于改进Co-Forest的主机故障预警方法[J].电子设计工程,2017,25(5):65-69. ZOU Bao-ping,QI Wei-qiang.Fault alarming method for host hardware based on improved Co-Forest[J].Electronic Design Engineering,2017,25(5):65-69.(in Chinese)
[12] JIA P,HUANG T,DUAN S,et al.A novel semi-supervised electronic nose learning technique:M-Training[J].Sensors,2016,16(3):370.
[13] 杨艺,韩德强,韩崇昭.一种基于证据距离的多分类器差异性度量[J].航空学报,2012,33(6):1093-1099. YANG Yi,HAN De-qiang,HAN Chong-zhao.A novel diversity measure of multiple classifier systems based on distance of evidence[J].Acta Aeronautica et Astronautica Sinica,2012,33(6):1093-1099.(in Chinese)
[14] 韩德强,杨艺,韩崇昭.DS证据理论研究进展及相关问题探讨[J].控制与决策,2014,29(1):1-11. HAN De-qiang,YANG Yi,HAN Chong-zhao.Advances in DS evidence theory and related discussions[J].Control and Decision,2014,29(1):1-11.(in Chinese)
[15] 杜利敏,徐扬.基于证据理论的不平衡数据半监督分类方法[J].计算机应用研究,2018,35(2):342-345. DU Li-min,XU Yang.Semi-supervised classification method for imbalanced data based on evidence theory[J].Application Research of Computers,2018,35(2):342-345.(in Chinese)
[16] LIU Z,PAN Q,MERCIER G,et al.A new incomplete pattern classification method based on evidential reasoning[J].IEEE Transactions on Cybernetics,2015,45(4):635-646.
[17] 杨新武,马壮,袁顺.基于弱分类器调整的多分类Adaboost算法[J].电子与信息学报,2016,38(2):373-380. YANG Xin-wu,MA Zhuang,YUAN Shun.Multi-class Adaboost algorithm based on the adjusted weak classifier[J].Journal of Electronics & Information Technology,2016,38(2):373-380.(in Chinese)
[18] 孙伟超,许爱强,李文海.区间信度结构下的证据合成方法研究[J].电子学报,2016,44(11):2726-2734. SUN Wei-chao,XU Ai-qiang,LI Wen-hai.Approaches for combination of interval-valued belief structures[J].Acta Electronica Sinica,2016,44(11):2726-2734.(in Chinese)
[19] 郭强,何友,关欣,等.一种多子焦元信度赋值非零情况下的DSmT近似融合推理方法[J].电子学报,2015,43(10):2069-2075. GUO Qiang,HE You,GUAN Xin,et al.An DSmT approximate reasoning method on the condition of non-zero multiple focal elements[J].Acta Electronica Sinica,2015,43(10):2069-2075.(in Chinese)
[20] SMETS P,KENNES R.The transferable belief model[J].Artificial Intelligence,1994,66(2):191-234.
[21] UCI machine learning repository[DB].http://archive.ics.uci.edu/ml/datasets,2017-10-10.
[22] PLATT J C.Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods[J].Advances in Large Margin Classifiers,2000,10(4):61-74.

一种基于证据理论的多类半监督分类算法

A Multi-class Semi-Supervised Classification Algorithm Based on Evidence Theory

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