Combined outputs framework for twin support vector machines

详细信息    查看全文

• 作者：Yuan-Hai Shao ; Xiang-Yu Hua ; Li-Ming Liu ; Zhi-Min Yang&#8230
• 关键词：Pattern recognition ; Support vector machines ; Twin support vector machines ; Optimization ; Heuristic algorithm
• 刊名：Applied Intelligence
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：43
• 期：2
• 页码：424-438
• 全文大小：1,105 KB
• 参考文献：1.Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20:273鈥?97
  2.Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:1鈥?3View Article
  3.Bradley PS, Mangasarian OL (2000) Massive data discrimination via linear support vector machines. Discret Methods Softw 13:1鈥?0MathSciNet View Article
  4.Zheng BC, Sang WY, Sarah SL (2014) Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst Appl 41(4):1476鈥?482View Article
  5.Li YX, Shao YH, Deng NY (2011) Improved prediction of palmitoylation sites using PWMs and SVM. Protein Pept Lett 18(2):186鈥?93View Article
  6.Scholkopf B, Tsuda K, Vert JP (2004) Kernel methods in computational biology. MIT Press, Cambridge
  7.Rai H, Anamika Y (2014) Iris recognition using combined support vector machine and hamming distance approach. Expert Syst Appl 41(2):588鈥?93View Article
  8.Xia SY, Xiong ZY, Luo YG, Dong LM, Xing CY (2015) Relative density based support vector machine. Neurocomputing 149:1424鈥?432View Article
  9.Jayadeva RK, Chandra S (2009) Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst Appl 36(1):132鈥?38MathSciNet View Article
  10.Deng NY, Tian YJ, Zhang CH (2012) Support vector machines: optimization based theory, algorithms, and extensions. CRC Press
  11.Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods. Mass.: Cambridge University Press, CambridgeView Article
  12.Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Int 28(1):69鈥?4View Article
  13.Jayadeva RK, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Int 29(5):905鈥?10View Article
  14.Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962鈥?68View Article
  15.Shao YH, Chen WJ, Deng NY (2014) Nonparallel hyperplane support vector machine for binary classification problems. Inf Sci 263(1):22鈥?5MathSciNet View Article
  16.Tian YJ, Qi ZQ, Ju XC, Shi Y, Liu XH (2014) Nonparallel support vector machines for pattern classification. IEEE Trans Cybern 44(7):1067鈥?9View Article
  17.Shao YH, Wang Z, Chen WJ, Deng NY (2013) A regularization for the projection twin support vector machine. Knowl-Based Syst 37:203鈥?10View Article
  18.Yang ZM, Hua XY, Shao YH (2012) Empirical analysis of adaboost twin bounded support vector machines. J Inf Comput Sci 9(16):5085鈥?092
  19.Shao YH, Deng NY, Yang ZM (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recog 45(6):2299鈥?307View Article
  20.Shao YH, Deng NY (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25:114鈥?21View Article
  21.Qi ZQ, Tian YJ, Shi Y (2013) Robust twin support vector machine for pattern classification. Pattern Recogn 46(1):305鈥?16View Article
  22.Ghorai S, Hossain SJ, Mukherjee A, Dutta PK (2010) Unity norm twin support vector machine classifier.India Conference (INDICON), India, pp 1-4
  23.Ghorai S, Hossain SJ, Mukherjee A, Dutta PK (2010) Newton鈥檚 method for nonparallel plane proximal classifier with unity norm hyperplanes. Signal Process 90(1):93鈥?04View Article
  24.Shao YH, Deng NY, Yang ZM, Chen WJ, Wang Z (2012) Probabilistic outputs for twin support vector machines. Knowl Based Syst 33:145鈥?51View Article
  25.Shao YH, Deng NY (2013) A novel margin based twin support vector machine with unity norm hyperplanes. Neural Comput Applic 22(7-8):1627鈥?635View Article
  26.Chew SW, Lucey S, Lucey P, Sridharan S, Conn JF (2012) Improved facial expression recognition via unihyperplane classification. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, pp 2554鈥?561
  27.Shao YH, Deng N , Chen WJ (2014) Laplacian unit-hyperplane learning from positive and unlabeled examples. Information Sciences, revised
  28.Shao YH, Deng NY, Chen WJ (2013) A proximal classifier with consistency. Knowl-Based Syst 49 (0):171鈥?78View Article
  29.Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd (Ed.) John Wiley and Sons, New York
  30.Kirkpatrick S (1984) Optimization by simulated annealing: Quantitative studies. J Stat Phys 34(5):975鈥?86MathSciNet View Article
  31.De BT, Cristianini N (2004) Convex methods for transduction. Adv Neural Inf Process Syst 16:73鈥?0
  32.Li XC, Wang L, Sung E (2008) AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell 21(5):785鈥?95View Article
  33.Li YF, Zhou ZH (2011) Towards making unlabeled data never hurt. Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue, WA, pp 1081鈥?088
  34.Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press
  35.MATLAB (2007) The MathWorks, Inc. http://鈥媤ww.鈥媘athworks.鈥媍om
  36.Blake CL, Merz CJ (1998) UCI Repository for Machine Learning Databases. Dept. of Information and Computer Sciences,Univ. of California, Irvine, http://鈥媤ww.鈥媔cs.鈥媢ci.鈥媏du/鈥媘learn/鈥婱LRepository.鈥媓tml
  37.Glenn F, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp 77鈥?6
  38.Chang CC, Lin CJ (2001) LIBSVM: A library for support vector machines. http://鈥媤ww.鈥媍sie.鈥媙tu.鈥媏du.鈥媡w/鈥媍jlin
  39.Glenn F, Mangasarian OL (2001) PSVM:Proximal support vector machine classifiers. http://鈥媟esearch.鈥媍s.鈥媤isc.鈥媏du/鈥媎mi/鈥媠vm/鈥媝svm/鈥?/span>
  40.Demsar J (2006) Statistical comparisons of classifiers over mul-tiple data sets. J Mac Learn Res 7:1鈥?0MathSciNet
  41.Garcia S, Herrera F (2008) An extension on 鈥渟tatistical com-parisons of classifiers over multiple data sets鈥?for all pairwise comparisons. J Mac Learn Res 9:2677鈥?694
  42.Garcia S, Fernandez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 9:2044鈥?064View Article
  43.Laarhoven PJM (1987) Simulated Annealing: Theory and Applications. Springer
  44.Sindhwani V, Keerthi SS, Chapelle O (2006) Deterministic annealing for semi-supervised kernel machines. Proceedings of the 23rd international conference on Machine learning, pp 841鈥?48
  45.Chapelle O, Tubingen G, Sindhwani V, Keerthi SS (2007) Branch and bound for semi-supervised support vector machines. In NIPS 20:217鈥?24
  
• 作者单位：Yuan-Hai Shao (1)
  Xiang-Yu Hua (2)
  Li-Ming Liu (3)
  Zhi-Min Yang (1)
  Nai-Yang Deng (4)
  
  1. Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, People鈥檚 Republic of China
  2. College of Economics and Management, Zhejiang University of Technology, Hangzhou, 310024, People鈥檚 Republic of China
  3. School of Statistics, Capital University of Economics and Business, Beijing, 100070, People鈥檚 Republic of China
  4. College of Science, China Agricultural University, Beijing, 100083, People鈥檚 Republic of China
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

Twin support vector machine (TWSVM) is regarded as a milestone in the development of powerful SVMs. However, there are some inconsistencies with TWSVM that can lead to many reasonable modifications with different outputs. In order to obtain better performance, we propose a novel combined outputs framework that combines rational outputs. Based on this framework, an optimal output model, called the linearly combined twin bounded support vector machine (LCTBSVM), is presented. Our LCTBSVM is based on the outputs of several TWSVMs, and produces the optimal output by solving an optimization problem. Furthermore, two heuristic algorithms are suggested in order to solve the optimization problem. Our comprehensive experiments show the superior generalization performance of our LCTBSVM compared with SVM, PSVM, GEPSVM, and some current TWSVMs, thus confirming the value of our theoretical analysis approach.