Least squares support vector machines with fast leave-one-out AUC optimization on imbalanced prostate cancer data

[1]  Thorsten Joachims,et al.  A support vector method for multivariate performance measures , 2005, ICML.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Song-hao Liu,et al.  Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine , 2014 .

[4]  J. Richie,et al.  Selection of optimal prostate specific antigen cutoffs for early detection of prostate cancer: receiver operating characteristic curves. , 1994, The Journal of urology.

[5]  W. A. Soanes,et al.  Precipitating antibody in the sera of patients treated cryosurgically for carcinoma of the prostate. , 1969, Experimental medicine and surgery.

[6]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[7]  Xi-Zhao Wang,et al.  Intuitionistic Fuzzy Twin Support Vector Machines , 2019, IEEE Transactions on Fuzzy Systems.

[8]  Nitesh V. Chawla,et al.  SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS , 2004 .

[9]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

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

[11]  Chi-Hyuck Jun,et al.  Instance categorization by support vector machines to adjust weights in AdaBoost for imbalanced data classification , 2017, Inf. Sci..

[12]  Bhavani Raskutti,et al.  Optimising area under the ROC curve using gradient descent , 2004, ICML.

[13]  James T. Kwok,et al.  Simplifying Mixture Models Through Function Approximation , 2006, IEEE Transactions on Neural Networks.

[14]  Gavin C. Cawley,et al.  Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[15]  Mehmet Engin,et al.  Early prostate cancer diagnosis by using artificial neural networks and support vector machines , 2009, Expert Syst. Appl..

[16]  Zhi-Hua Zhou,et al.  One-Pass AUC Optimization , 2013, ICML.

[17]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[18]  Xuan Wang,et al.  Research on classification method of high-dimensional class-imbalanced datasets based on SVM , 2019, Int. J. Mach. Learn. Cybern..

[19]  Siwei Lyu,et al.  Stochastic Online AUC Maximization , 2016, NIPS.

[20]  Ulf Brefeld,et al.  Co-EM support vector learning , 2004, ICML.

[21]  Kurt S. Riedel,et al.  A Sherman-Morrison-Woodbury Identity for Rank Augmenting Matrices with Application to Centering , 1992, SIAM J. Matrix Anal. Appl..

[22]  Rong Jin,et al.  Online AUC Maximization , 2011, ICML.

[23]  Mehryar Mohri,et al.  AUC Optimization vs. Error Rate Minimization , 2003, NIPS.

[24]  Zhi-Hua Zhou,et al.  On the Consistency of AUC Pairwise Optimization , 2012, IJCAI.

[25]  Wenli Du,et al.  Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems , 2019, Appl. Soft Comput..

[26]  Ying Liu,et al.  Active Learning with Support Vector Machine Applied to Gene Expression Data for Cancer Classification , 2004, J. Chem. Inf. Model..

[27]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[28]  G. Murphy,et al.  Prostatic‐specific antigen: An immunohistologic marker for prostatic neoplasms , 1981, Cancer.

[29]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[30]  Alain Rakotomamonjy,et al.  Optimizing Area Under Roc Curve with SVMs , 2004, ROCAI.

[31]  Kup-Sze Choi,et al.  Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[32]  Hadi Sadoghi Yazdi,et al.  Online neural network model for non-stationary and imbalanced data stream classification , 2014, Int. J. Mach. Learn. Cybern..

[33]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[34]  Wentao Mao,et al.  An ELM-based model with sparse-weighting strategy for sequential data imbalance problem , 2016, International Journal of Machine Learning and Cybernetics.

[35]  Masoom A. Haider,et al.  Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields , 2010, IEEE Transactions on Image Processing.

[36]  J. Richie,et al.  COMPARISON OF DIGITAL RECTAL EXAMINATION AND SERUM PROSTATE SPECIFIC ANTIGEN IN THE EARLY DETECTION OF PROSTATE CANCER: RESULTS OF A MULTICENTER CLINICAL TRIAL OF 6,630 MEN , 1994, The Journal of urology.

[37]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[38]  Tapio Salakoski,et al.  Efficient AUC Maximization with Regularized Least-Squares , 2008, SCAI.

[39]  Kup-Sze Choi,et al.  A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data , 2020, IEEE Transactions on Cybernetics.

[40]  Szymon Jaroszewicz,et al.  Efficient AUC Optimization for Classification , 2007, PKDD.