Applications Based on TWSVM

TWSVM and its variants have been widely discussed in earlier chapters. In this chapter, we tend to discuss the applications where the special properties of TWSVMs have been used. One of the major advantage of TWSVM is its superiority over other machine learning methodologies in dealing with unbalanced datasets. Such datasets naturally arise for e.g. in medical domain where samples of diseased patients is far less than normal patients resulting in unbalanced classes. The structure of TWSVM allows to identify the hyperplane close to samples of diseased patients. Thus via this chapter, we tend to make reader familiar with widespread applicability of TWSVM across a multitude of application domain.

[1]  S P Arjunan,et al.  A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[2]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[3]  Saeed Jalili,et al.  Hierarchical Least Square Twin Support Vector Machines Based Framework for Human Action Recognition , 2011, 2011 7th Iranian Conference on Machine Vision and Image Processing.

[4]  Zhong-bao Liu,et al.  [Automatic classification method of star spectra data based on manifold fuzzy twin support vector machine]. , 2015, Guang pu xue yu guang pu fen xi = Guang pu.

[5]  Dong Hongbo,et al.  Application of Twin Support Vector Machine for Fault Diagnosis of Rolling Bearing , 2014 .

[6]  Jayadeva,et al.  High performance EEG signal classification using classifiability and the Twin SVM , 2015, Appl. Soft Comput..

[7]  Jalal A. Nasiri,et al.  Energy-based model of least squares twin Support Vector Machines for human action recognition , 2014, Signal Process..

[8]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[9]  Fang Su,et al.  A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction , 2013 .

[10]  Divya Tomar,et al.  An efficient Parkinson disease diagnosis system based on Least Squares Twin Support Vector Machine and Particle Swarm Optimization , 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS).

[11]  Reshma Khemchandani,et al.  Color image classification and retrieval through ternary decision structure based multi-category TWSVM , 2015, Neurocomputing.

[12]  Taeho Jo,et al.  A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..

[13]  Xinbo Gao,et al.  MCs Detection with Combined Image Features and Twin Support Vector Machines , 2009, J. Comput..

[14]  Zhichao Li,et al.  High Efficient Intrusion Detection Methodology with Twin Support Vector Machines , 2008, 2008 International Symposium on Information Science and Engineering.

[15]  Taeho Jo,et al.  Class imbalances versus small disjuncts , 2004, SKDD.

[16]  Saeed Jalili,et al.  Action Recognition by Local Space-Time Features and Least Square Twin SVM (LS-TSVM) , 2011, 2011 First International Conference on Informatics and Computational Intelligence.

[17]  Divya Tomar,et al.  Prediction of software defects using Twin Support Vector Machine , 2014, 2014 International Conference on Information Systems and Computer Networks (ISCON).

[18]  Xiangyang Wang,et al.  Image denoising using nonsubsampled shearlet transform and twin support vector machines , 2014, Neural Networks.

[19]  Di He,et al.  A probability approach to anomaly detection with twin support vector machines , 2010 .

[20]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[21]  Taghi M. Khoshgoftaar,et al.  Experimental perspectives on learning from imbalanced data , 2007, ICML '07.

[22]  Mujahed Al-Dhaifallah,et al.  Identification of Auto-Regressive Exogenous Models Based on Twin Support Vector Machine Regression , 2013 .

[23]  Maoxiang Chu,et al.  Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine , 2014 .

[24]  Divya Tomar,et al.  Feature Selection based Least Square Twin Support Vector Machine for Diagnosis of Heart Disease , 2014, BSBT 2014.

[25]  Xinsheng Zhang,et al.  Boosting Twin Support Vector Machine Approach for MCs Detection , 2009, 2009 Asia-Pacific Conference on Information Processing.

[26]  Dinesh Kant Kumar,et al.  Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal , 2016, Biomedizinische Technik. Biomedical engineering.

[27]  Ying Wang,et al.  TWin support tensor machines for MCs detection , 2009 .

[28]  Haofeng Zhang,et al.  A Fast Incremental Learning Algorithm Based on Twin Support Vector Machine , 2014, 2014 Seventh International Symposium on Computational Intelligence and Design.

[29]  Reshma Khemchandani,et al.  Robust least squares twin support vector machine for human activity recognition , 2016, Appl. Soft Comput..

[30]  Ping Xue,et al.  Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[31]  Ganesh R. Naik,et al.  Twin SVM for Gesture Classification Using the Surface Electromyogram , 2010, IEEE Transactions on Information Technology in Biomedicine.

[32]  Ravi Kothari,et al.  Classifiability-based omnivariate decision trees , 2005, IEEE Transactions on Neural Networks.

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

[34]  C. A. Dairaghi,et al.  Concurrent neuromechanical and functional gains following upper-extremity power training post-stroke , 2013, Journal of NeuroEngineering and Rehabilitation.

[35]  Jin Ding,et al.  License Plate Location Algorithm Based on Twin SVM , 2011 .

[36]  Rosa Maria Valdovinos,et al.  The Imbalanced Training Sample Problem: Under or over Sampling? , 2004, SSPR/SPR.

[37]  Mingli Ding,et al.  Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine , 2013 .

[38]  Li Jun-tao.  Online Learning Algorithm for Least Squares Twin Support Vector Machines , 2012 .

[39]  Reshma Khemchandani,et al.  Incremental Twin Support Vector Machines , 2009 .

[40]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[41]  Lan Bai,et al.  Information Technology and Quantitative Management , ITQM 2013 Exploring determinants of inflation in China based on L 1-- twin support vector regression , 2013 .

[42]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[43]  Lu Jing,et al.  Mass Detection in Digital Mammograms Using Twin Support Vector Machine-Based CAD System , 2009, 2009 WASE International Conference on Information Engineering.

[44]  Ya-Fen Ye,et al.  Comparing Inflation Forecasts Using an $\varepsilon$-wavelet Twin Support Vector Regression , 2013 .

[45]  Vijay P. Singh,et al.  Towards identification of finger flexions using single channel surface electromyography – able bodied and amputee subjects , 2013, Journal of NeuroEngineering and Rehabilitation.