基于pinball损失的结构模糊孪生支持向量机

doi:10.3969/j.issn.0372-2112.2019.10.025

电子学报 ›› 2019, Vol. 47 ›› Issue (10): 2221-2227.DOI: 10.3969/j.issn.0372-2112.2019.10.025

基于pinball损失的结构模糊孪生支持向量机

李凯, 李慧

河北大学网络空间安全与计算机学院, 河北保定 071000

收稿日期:2019-05-16 修回日期:2019-06-15 出版日期:2019-10-25
- 作者简介:
- 李凯男,1963年生于河北保定.2005年毕业于北京交通大学计算机与信息技术学院,并获得工学博士学位.主要从事机器学习,模式识别,数据挖掘等方面研究.E-mail:likai@hbu.edu.cn;李慧女,1993年生于河北石家庄.硕士研究生.研究方向为机器学习与数据挖掘
- 基金资助:
- 河北省自然科学基金 (No.F2018201060）

Structural Fuzzy Twin Support Vector Machine with Pinball Loss

LI Kai, LI Hui

School of Cyber Security and Computer, Hebei University, Baoding, Hebei 071000, China

Received:2019-05-16 Revised:2019-06-15 Online:2019-10-25 Published:2019-10-25

摘要/Abstract

摘要： 孪生支持向量机通过求解较小的二次规划问题，提高了分类器的性能，然而，该方法主要利用了类间可分的特性，并使用hinge损失函数构建相应的模型，它们并未充分考虑不同类中数据的结构信息以及不同样本对分类的影响，导致该方法对噪声具有较强的敏感性以及重取样的不稳定性.为了进一步提高孪生支持向量机的性能，基于pinball损失函数，将数据集中不同类的结构信息以及不同样本的作用引入到孪生支持向量机中，获得了基于pinball损失的结构模糊孪生支持向量机模型，从理论上导出了基于pinball损失的结构模糊孪生支持向量机算法pin-sftsvm，通过选取人工生成数据集与UCI标准数据集，对pin-sftsvm算法进行了实验，并与tbsvm、s-tsvm和pin-tsvm算法进行了性能比较，表明了提出算法的有效性.

关键词: 结构信息, pinball损失, 模糊隶属度, 孪生支持向量机

Abstract: Twin support vector machine improves the performance of the classifier by solving the smaller quadratic programming problem.However,this method mainly utilizes the separability between classes and constructs the corresponding model using the hinge loss function.Not considering the structural information of the intra-class data and the influence of different samples on the classification,the method has strong sensitivity to noise and instability of resampling.In order to further improve the performance of the twin support vector machine,the structural information of different classes in the data and the effects of different samples are introduced into the twin support vector machine based on the pinball loss function,and the structure fuzzy support vector machine model based on pinball loss is obtained.The structural fuzzy twin support vector machine algorithm pin-sftsvm based on the pinball loss is derived theoretically.The presented algorithm pin-sftsvm is tested by selecting the artificially generated data set and the UCI standard data set,and compared with the tbsvm,s-tsvm and pin-tsvm algorithms.Experimental results show the effectiveness of the proposed algorithm.

Key words: structural information, pinball loss, membership degree of fuzzy, twin support vector machine

中图分类号:

TP391

李凯, 李慧. 基于pinball损失的结构模糊孪生支持向量机[J]. 电子学报, 2019, 47(10): 2221-2227.

LI Kai, LI Hui. Structural Fuzzy Twin Support Vector Machine with Pinball Loss[J]. Acta Electronica Sinica, 2019, 47(10): 2221-2227.

参考文献

[1] Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,2000.
[2] Scholkopf B,Smola A J,Williamson R C,et al.New support vector algorithms[J].Neural Computation,2000,12(5):1207-1245.
[3] Bloom V,Griva I,Kwon B,et al.Exterior-point method for support vector machines[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(7):1390-1393.
[4] Ding SF,Shi Z Z,Tao D C,et al.Recent advances in support vector machines[J].Neurocomputing,2016,211(c):1-3.
[5] 方佳艳,刘峤,吴德,秦志光.基于模糊C-均值的相似性特征转换光滑支持向量机[J].电子学报,2018,46(11):2714-2724. FANG Jiayan,LIU Qiao,WU De,QIN Zhiguang.Smooth support vector machine with similarity-based feature transformation technique and fuzzy c-means clustering[J].Acta Electronica Sinica,2018,46(11):2714-2724.(in Chinese)
[6] Lin C F,Wang S D.Fuzzy support vector machines[J].IEEE Transaction on Neural Networks,2002,13(2):464-471.
[7] Yang X W,Zhang G Q,Lu J.A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises[J].IEEE Transactions on Fuzzy Systems,2011,19(1):105-115.
[8] Xu Y T.A rough margin-based linear υ support vector regression[J].Statistics and Probability Letters,2012,82(3):528-534.
[9] Chen D G,He Q,Wang X Z.FRSVMs:Fuzzy rough set based support vector machines[J].Fuzzy Sets and Systems,2010,161(4):596-607.
[10] Fung G,Mangasarian O L.Proximal support vector machine classifiers[A].Proceedings of the 7th International Conference on Knowledge and Data Discovery[C].New York:ACM,2001.77-86.
[11] Mangasarian O L,Wild E W.Multisurface proximal support vector machine classification via generalized eigenvalues[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1):69-74.
[12] Jayadeva R K,Khemchandani R,Chandra S.Twin support vector machine for pattern classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[13] Shao Y H,Zhang C H,Wang X B,et al.Improvements on twin support vector machines[J].IEEE Transactions on Neural Networks,2011,22(6):962-968.
[14] Huang H J,Wei X X,Zhou Y Q.Twin support vector machines:A survey[J].Neurocomputing,2018,300:34-43.
[15] Ding S F,Zhang N,Zhang X K,et al.Twin support vector machine:theory,algorithm and applications[J].Neural Computing and Applications,2017,28(11):3119-3130.
[16] Ding S F,Yu J Z,Qi B J,et al.An overview on twin support vector machines[J].Artificial Intelligence Revie,2014,42(2):245-252.
[17] Tomar D,Agarwal S.Twin support vector machine:a review from 2007 to 2014[J].Egyptian Informatics Journal,2015,16(1):55-69.
[18] Peng X J.TPMSVM:A novel twin parametric-margin support vector machine for pattern recognition[J].Pattern Recognition,2011,44(10):2678-2692.
[19] Xu Y T,Pan X L,Zhou Z J.Structural least square twin support vector machine for classification[J].Applied Intelligence,2015,42(3):527-536.
[20] Qi Z Q,Tian Y J,Shi Y.Structural twin support vector machine for classification[J].Knowledge-Based Systems,2013,43(1):74-81.
[21] 陈素根,吴小俊.改进的投影孪生支持向量机[J].电子学报,2017,45(2):408-416. CHEN Sugen,WU Xiaoju.Improved projection twin supportvector machine[J].Acta Electronica Sinica,2017,45(2):408-416.(in Chinese)
[22] Xu Y T,Yang Z J,Pan X L.A novel twin support-vector machine with pinball loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370.
[23] Blake C,Merz C J.UCI Repository for machine learning databases[DB/OL].http://www.ics.uci.edu/mlearn/MLRepository.html,1998.

基于pinball损失的结构模糊孪生支持向量机

Structural Fuzzy Twin Support Vector Machine with Pinball Loss

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

[1]	杨小龙, 李欣玥, 周牧, 王勇, 何维. 基于多维模糊映射AP优化的WLAN室内定位方法[J]. 电子学报, 2022, 50(8): 1875-1884.
[2]	顾晓清, 倪彤光, 姜志彬, 王士同. 面向大规模噪声数据的软性核凸包支持向量机[J]. 电子学报, 2018, 46(2): 347-357.
[3]	李俊, 谢维信, 李良群, 刘钧彬. 基于模糊空时线索的多目标在线跟踪算法[J]. 电子学报, 2017, 45(3): 513-519.
[4]	陈素根, 吴小俊. 改进的投影孪生支持向量机[J]. 电子学报, 2017, 45(2): 408-416.
[5]	毕晓君, 张永建, 陈春雨. 基于模糊支配的高维多目标进化算法MFEA[J]. 电子学报, 2014, 42(8): 1653-1659.
[6]	储茂祥, 王安娜, 巩荣芬. 一种改进的最小二乘孪生支持向量机分类算法[J]. 电子学报, 2014, 42(5): 998-1003.
[7]	肖杰, 江建慧, 杨旭华, 梁家荣. 一个面向缺陷分析的电路成品率与可靠性的关系模型[J]. 电子学报, 2014, 42(4): 747-755.
[8]	吴晓军, 李功清. 基于样本和线性结构信息的大范围图像修复算法[J]. 电子学报, 2012, 40(8): 1509-1514.
[9]	肖杰;江建慧. 结合版图结构信息的基本门电路故障概率估计[J]. 电子学报, 2012, 40(2): 235-240.
[10]	王科俊;吕俊杰;冯伟兴;王鑫;贺波. 一种新的真核基因剪接位点识别方法[J]. 电子学报, 2011, 39(5): 1210-1213.
[11]	叶盛楠;苏开娜;肖创柏;段娟. 基于结构信息提取的图像质量评价[J]. 电子学报, 2008, 36(5): 856-861.