计算机科学 ›› 2018, Vol. 45 ›› Issue (11): 29-36.doi: 10.11896/j.issn.1002-137X.2018.11.003
安悦瑄1, 丁世飞1,2, 胡继普1
AN Yue-xuan1, DING Shi-fei1,2, HU Ji-pu1
摘要: 孪生支持向量机(Twin Support Vector Machine,TWSVM)是在支持向量机(Support Vector Machine,SVM)的基础上发展而来的一种新的机器学习方法。作为一种二分类的分类器,其基本思想为寻找两个超平面,使得每一个分类面靠近本类样本点而远离另一类样本点。作为一种新兴的机器学习方法,孪生支持向量机自提出以来便引起了国内外学者的广泛关注,已经成为机器学习领域的研究热点。对孪生支持向量机的最新研究进展进行综述,首先介绍了孪生支持向量机的基本概念与基本模型;然后对近几年来新型的孪生支持向量机模型与研究进展进行了总结,并对其代表算法进行了优缺点分析和实验比较;最后对将来的研究工作进行了展望。
中图分类号:
[1]CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297. [2]VAPNIK V N.统计学习理论的本质[M].张学工,译.北京:清华大学出版社,2000. [3]DING S F,QI B J,TAN H Y.An overview on theory and algorithm of support vector machines[J].Journal of University of Electronic Science and Technology of China,2011,40(1):2-10. [4]MANGASARIAN O L,WILD E W.Multisurface proximal support vector machine classification via generalized eigenvalues[J].IEEE Trans. Pattern Anal. Mach. Intell.,2006,28(1):69-74. [5]JAYADEVA,KHEMCHANDNI R,SURESH C.Twin support vector machines for pattern classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910. [6]DING S F,HUA X P,YU J Z.An overview on nonparallel hyperplane support vector machine algorithms[J].Neural Computing and Applications,2014,25(5):975-982. [7]TOMAR D,AGARWAL S.Twin Support Vector Machine:A review from 2007 to 2014[J].Egyptian Informatics Journal,2015,16(1):55-69. [8]TIAN Y,QI Z.Review on:Twin Support Vector Machines[J].Annals of Data Science,2014,1(2):253-277. [9]KUMAR M A,GOPAL M.Least squares twin support vector machines for pattern classification[J].Expert Systems with Applications,2009,36(4):7535-7543. [10]FUNG G,MANGASARIAN O L.Proximal support vector machine classifiers[C]∥Proceedings of Seventh International Conference on Knowledge and Data Discovery.2001:77-86. [11]ZHANG Z,ZHEN L,DENG N,et al.Sparse least square twin support vector machine with adaptive norm[J].Applied Intelligence,2014,41(4):1097-1107. [12]TANVEER M,KHAN M A,HO S S.Robust energy-based least squares twin support vector machines[J].Applied Intelligence,2016,45(1):174-186. [13]XU Y,PAN X,ZHOU Z,et al.Structural least square twin support vector machine for classification[J].Applied Intelligence,2015,42(3):527-536. [14]YE Q,ZHAO C,GAO S,et al.Weighted twin support vector machines with local information and its application[J].Neural Networks the Official Journal of the International Neural Network Society,2012,35(11):31-39. [15]REZVANI-KHORASHADIZADEH R,REZA M.WS-TWS- VM:Weighted Structural Twin Support Vector Machine by local and global information[C]∥International Conference on Computer and Knowledge Engineering.2015:170-175. [16]XU Y.K-nearest neighbor-based weighted multi-class twin support vector machine[M].Elsevier Science Publishers B.V.2016. [17]CHEN X,YANG J,YE Q,et al.Recursive projection twin support vector machine via within-class variance minimization[J].Pattern Recognition,2011,44(10):2643-2655. [18]SHAO Y H,WANG Z,CHEN W J,et al.A regularization for the projection twin support vector machine[J].Knowledge-Based Systems,2013,37(2):203-210. [19]DING S F,HUA X P.Recursive least squares projection twin support vector machines for nonlinear classification[J].Neurocomputing,2014,130(3):3-9. [20]XIE X.Improvement on projection twin support vector machine[J].Neural Computing & Applications,2017(5):1-17. [21]YAN A R,YE B Q,ZHANG C L,et al.A Feature Selection Method for Projection Twin Support Vector Machine[J].Neural Processing Letters,2017(3):1-18. [22]GU Z,ZHANG Z,SUN J.Robust Image Recognition by L1- norm Twin-Projection Support Vector Machine[J].Neurocomputing,2017,223:1-11. [23]DING S F,ZHANG J,ZHANG X K,et al.Survey on Multi class Twin Support Vector Machines[J].Journal of Software,2018,29(1):89-108.(in Chinese) 丁世飞,张健,张谢锴,等.多分类孪生支持向量机研究进展[J].软件学报,2018,29(1):89-108. [24]TOMAR D,AGARWAL S.An effective Weighted Multi class Least Squares Twin Support Vector Machine for Imbalanced data classification[J].International Journal of Computational Intelligence Systems,2015,8(4):761-778. [25]CHEN S G,WU X J.Multiple birth least squares support vector machine for multi-class classification[J].International Journal of Machine Learning & Cybernetics,2017,8(6):1731-1742. [26]KHEMCHANDANI R,PAL A.Tree based multi-category Laplacian TWSVM for content based image retrieval[J].International Journal of Machine Learning & Cybernetics,2017,8(4):1197-1210. [27]DING S F,ZHANG X K,AN Y X,et al.Weighted Linear Loss Multiple Birth Support Vector Machine based on Information Granulation for Multi-class Classification[J].Pattern Recognition,2017,67:32-46. [28]GU H B,NIU B,GAO Z X.A directed acyclic graph algorithm for multi-class classification based on twin support vector machine[J].Journal of Information & Computational Science,2014,11(18):6529-6536. [29]CHEN J,JI G R.Multi class LSTSVM classifier based on optimal directed acyclic graph[C]∥International Conference on Computer and Automation Engineering.IEEE,2010:100-104. [30]ZHANG X,DING S,SUN T.Multi-class LSTMSVM based on optimal directed acyclic graph and shuffled frog leaping algorithm[J].International Journal of Machine Learning & Cybernetics,2016,7(2):241-251. [31]XU Y T,GUO R,WANG L S.A twin multi class classification support vector machine[J].Cognitive Computation,2013,5(4):580-588. [32]PARASTALOOI N,AMIRI A,ALIHEYDARI P.Modified Twin Support Vector Regression[J].Neurocomputing,2016,211(C):84-97. [33]SARTAKHTI J S,AFRABANDPEY H,SARAEE M.Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification[J].Soft Computing,2017,21(15):4361-4373. [34]DING S F,ZHANG X K,YU J Z.Twin support vector machines based on fruit fly optimization algorithm[J].International Journal of Machine Learning and Cybernetics,2016,7(2):1-11. [35]ZHANG X K,DING S F.Mahalanobis Distance-based Twin Multi-class Classification Support Vector Machine[J].Compu-ter Science,2016,43(3):49-53.(in Chinese) 张谢锴,丁世飞.基于马氏距离的孪生多分类支持向量机[J].计算机科学,2016,43(3):49-53. [36]DING S F,AN Y X,ZHANG X K,et al.Wavelet twin support vector machines based on glowworm swarm optimization[J].Neurocomputing,2017,225(15):157-163. [37]XIE X,SUN S.Multi-view twin support vector machines[J].Intelligent Data Analysis,2015,19(4):701-712. [38]CHEN W J,SHAO Y H,LI C N,et al.MLTSVM:A novel twin support vector machine to multi-label learning[J].Pattern Re-cognition,2016,52:61-74. [39]CHEN S,WU X,ZHANG R.A Novel Twin Support Vector Machine for Binary Classification Problems[J].Neural Proces-sing Letters,2016,44(3):1-17. [40]HAO P Y.Pair-v-SVR:A Novel and Efficient Pairing nu-Support Vector Regression Algorithm[J].IEEE Transactions on Neural Networks & Learning Systems,2017,28(11):2503-2510. [41]PENG X J,SHEN J D.A twin hyperspheres support vector machine with automatic variable weights for data classification[J].Information Science,2017,417:216-235. [42]GUPTA D.Training primal K-nearest neighbor based weighted twin support vector regression via unconstrained convex minimization[J].Applied Intelligence,2017,47(3):962-991. [43]GAO X,SUN Q,XU H.Multiple Instance Learning via Semi-supervised Laplacian TSVM[J].Neural Processing Letters,2017,46(1):219-232. [44]TANVEER M,SHUBHAM K.A regularization on Lagrangian twin support vector regression[J].International Journal of Machine Learning & Cybernetics,2017,8(3):807-821. [45]XU Y T.Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification[J].IEEE Transactions on Cybernetics,2017,47(6):1540-1550. [46]SAIGAL P,KHANNA V.Divide and conquer approach for semi-supervised multi-category classification through localized kernel spectral clustering[J].Neurocomputing,2017,238:296-306. [47]LI J,CAO Y,WANG Y,et al.Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines[J].Neural Processing Letters,2017,45(1):319-339. [48]PENG X,KONG L,CHEN D.A structural information-based twin-hypersphere support vector machine classifier[J].International Journal of Machine Learning & Cybernetics,2017,8(1):295-308. [49]BALASUNDARAM S,GUPTA D,PRASAD S C.A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization[J].Applied Intelligence,2017,46(1):124-134. [50]PAN X,YANG Z,XU Y,et al.Safe Screening Rules for Acce- lerating Twin Support Vector Machine Classification[J].IEEE Transactions on Neural Networks & Learning Systems,2018,29(5):1876-1887. [51]HAN R J,CAO Q L.Fuzzy chance constrained least squares twin support vector machine for uncertain classification[J].Journal of Inteligent & Fuzzy Systems,2017,33(5):3041-3049. [52]XU Y T,YANG Z J,et al.A Novel Twin Support Vector Machine with Pinball Loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370. [53]CAO L,SHEN H.Imbalanced data classification based on hybrid resampling and twin support vector machine[J].Computer Science & Information Systems,2017,14(3):579-595. [54]WANG H,ZHOU Z.An improved rough margin-based twin bounded support vector machine[M].Elsevier Science Publi-shers B.V.,2017:125-138. |
[1] | 侯夏晔, 陈海燕, 张兵, 袁立罡, 贾亦真. 一种基于支持向量机的主动度量学习算法 Active Metric Learning Based on Support Vector Machines 计算机科学, 2022, 49(6A): 113-118. https://doi.org/10.11896/jsjkx.210500034 |
[2] | 黄国兴, 杨泽铭, 卢为党, 彭宏, 王静文. 利用粒子滤波方法求解数据包络分析问题 Solve Data Envelopment Analysis Problems with Particle Filter 计算机科学, 2022, 49(6A): 159-164. https://doi.org/10.11896/jsjkx.210600110 |
[3] | 单晓英, 任迎春. 基于改进麻雀搜索优化支持向量机的渔船捕捞方式识别 Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm 计算机科学, 2022, 49(6A): 211-216. https://doi.org/10.11896/jsjkx.220300216 |
[4] | 陈景年. 一种适于多分类问题的支持向量机加速方法 Acceleration of SVM for Multi-class Classification 计算机科学, 2022, 49(6A): 297-300. https://doi.org/10.11896/jsjkx.210400149 |
[5] | 邢云冰, 龙广玉, 胡春雨, 忽丽莎. 基于SVM的类别增量人体活动识别方法 Human Activity Recognition Method Based on Class Increment SVM 计算机科学, 2022, 49(5): 78-83. https://doi.org/10.11896/jsjkx.210400024 |
[6] | 武玉坤, 李伟, 倪敏雅, 许志骋. 单类支持向量机融合深度自编码器的异常检测模型 Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder 计算机科学, 2022, 49(3): 144-151. https://doi.org/10.11896/jsjkx.210100142 |
[7] | 潘燕娜, 冯翔, 虞慧群. 基于自适应资源分配池的竞争合作群协同优化算法 Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool 计算机科学, 2022, 49(2): 182-190. https://doi.org/10.11896/jsjkx.201200012 |
[8] | 侯春萍, 赵春月, 王致芃. 基于自反馈最优子类挖掘的视频异常检测算法 Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining 计算机科学, 2021, 48(7): 199-205. https://doi.org/10.11896/jsjkx.200800146 |
[9] | 郭福民, 张华, 胡瑢华, 宋岩. 一种基于表面肌电信号的腕部肌力估计方法研究 Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals 计算机科学, 2021, 48(6A): 317-320. https://doi.org/10.11896/jsjkx.200600021 |
[10] | 卓雅倩, 欧博. 噪声环境下的人脸防伪识别算法研究 Face Anti-spoofing Algorithm for Noisy Environment 计算机科学, 2021, 48(6A): 443-447. https://doi.org/10.11896/jsjkx.200900207 |
[11] | 雷剑梅, 曾令秋, 牟洁, 陈立东, 王淙, 柴勇. 基于整车EMC标准测试和机器学习的反向诊断方法 Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning 计算机科学, 2021, 48(6): 190-195. https://doi.org/10.11896/jsjkx.200700204 |
[12] | 李笠, 李广鹏, 常亮, 古天龙. 约束进化算法及其应用研究综述 Survey of Constrained Evolutionary Algorithms and Their Applications 计算机科学, 2021, 48(4): 1-13. https://doi.org/10.11896/jsjkx.200600151 |
[13] | 王友卫, 朱晨, 朱建明, 李洋, 凤丽洲, 刘江淳. 基于用户兴趣词典和LSTM的个性化情感分类方法 User Interest Dictionary and LSTM Based Method for Personalized Emotion Classification 计算机科学, 2021, 48(11A): 251-257. https://doi.org/10.11896/jsjkx.201200202 |
[14] | 曹素娥, 杨泽民. 基于聚类分析算法和优化支持向量机的无线网络流量预测 Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine 计算机科学, 2020, 47(8): 319-322. https://doi.org/10.11896/jsjkx.190800075 |
[15] | 刘肖, 袁冠, 张艳梅, 闫秋艳, 王志晓. 基于自适应多分类器融合的手势识别 Hand Gesture Recognition Based on Self-adaptive Multi-classifiers Fusion 计算机科学, 2020, 47(7): 103-110. https://doi.org/10.11896/jsjkx.200100073 |
|