Improved universum twin support vector machine

Universum based learning provides prior information about data in the optimization problem of support vector machine (SVM). Universum twin support vector machine (UTSVM) is a computationally efficient algorithm for classification problems. It solves a pair of quadratic programming problems (QPPs) to obtain the classifier. In order to include the structural risk minimization (SRM) principle in the formulation of UTSVM, we propose an improved universum twin support vector machine (IUTSVM). Our proposed IUTSVM implicitly makes the matrices non-singular in the optimization problem by adding a regularization term. Several numerical experiments are performed on benchmark real world datasets to verify the efficacy of our proposed IUTSVM. The experimental results justifies the better generalization performance of our proposed IUTSVM in comparison to existing algorithms.

[1]  Muhammad Tanveer.  Robust and Sparse Linear Programming Twin Support Vector Machines , 2014, Cognitive Computation.

[2]  Ivor W. Tsang,et al.  Efficient kernel feature extraction for massive data sets , 2006, KDD '06.

[3]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[5]  Dong Xu,et al.  Structural regularized projection twin support vector machine for data classification , 2014, Inf. Sci..

[6]  S. Balasundaram,et al.  On Lagrangian twin support vector regression , 2012, Neural Computing and Applications.

[7]  Muhammad Tanveer,et al.  EEG signal classification using universum support vector machine , 2018, Expert Syst. Appl..

[8]  Jason Weston,et al.  Inference with the Universum , 2006, ICML.

[9]  Chien-Liang Liu,et al.  Semi-Supervised Text Classification With Universum Learning , 2016, IEEE Transactions on Cybernetics.

[10]  Yuan-Hai Shao,et al.  Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.

[11]  Sonali Agarwal,et al.  Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes , 2015, Adv. Artif. Neural Syst..

[12]  Vladimir Cherkassky,et al.  Gender classification of human faces using inference through contradictions , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[13]  Yong Shi,et al.  Twin support vector machine with Universum data , 2012, Neural Networks.

[14]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[15]  Muhammad Tanveer,et al.  A robust fuzzy least squares twin support vector machine for class imbalance learning , 2018, Appl. Soft Comput..

[16]  Wen Long,et al.  Investor sentiment identification based on the universum SVM , 2018, Neural Computing and Applications.

[17]  Muhammad Tanveer,et al.  Newton method for implicit Lagrangian twin support vector machines , 2015, Int. J. Mach. Learn. Cybern..

[18]  Reshma Khemchandani,et al.  Angle-based twin support vector machine , 2017, Annals of Operations Research.

[19]  Marie Chupin,et al.  Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Muhammad Tanveer,et al.  Robust energy-based least squares twin support vector machines , 2015, Applied Intelligence.

[21]  Yuan-Hai Shao,et al.  A regularization for the projection twin support vector machine , 2013, Knowl. Based Syst..

[22]  Muhammad Tanveer,et al.  An efficient implicit regularized Lagrangian twin support vector regression , 2015, Applied Intelligence.

[23]  Bharat Richhariya,et al.  A Fuzzy Universum Support Vector Machine Based on Information Entropy , 2018, Advances in Intelligent Systems and Computing.

[24]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[25]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[26]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[27]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[28]  Muhammad Tanveer.  Application of smoothing techniques for linear programming twin support vector machines , 2014, Knowledge and Information Systems.