Imbalanced and semi-supervised classification for prognosis of ACLF

Acute-on-chronic liver failure (ACLF) is characterized by jaundice, coagulopathy, hepatic encephalopathy, and associated with high mortality. According to the progress of patients, we partition 81 ACLF patients into three groups. Group I includes 40 improved patients, group II contains 18 death patients, and group III is composed of 23 unlabeled patients. For the imbalanced characteristic of groups I and II, we construct an imbalanced prediction model based on small sphere and large margin approach (SSLM). SSLM classifies two classes of samples by maximizing their margin and then is an effective classification method for imbalanced data. For groups I, II and III, we present a prediction model based on semi-supervised twin support vector machine (TSVM), which integrates 23 unlabeled samples into the training process and improves testing accuracy. Compared with other three algorithms, our two proposed prediction models produce better testing accuracy. Finally we apply them to predict 23 not confirmed patients, and integrate them with the MELD method to obtain their prediction labels.

[1]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

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

[3]  Xijin Tang,et al.  Text classification based on multi-word with support vector machine , 2008, Knowl. Based Syst..

[4]  Madan Gopal,et al.  Application of smoothing technique on twin support vector machines , 2008, Pattern Recognit. Lett..

[5]  T. Therneau,et al.  A model to predict survival in patients with end‐stage liver disease , 2001 .

[6]  Olvi L. Mangasarian,et al.  Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[8]  Yong Shi,et al.  Laplacian twin support vector machine for semi-supervised classification , 2012, Neural Networks.

[9]  Yitian Xu,et al.  A rough margin-based one class support vector machine , 2012, Neural Computing and Applications.

[10]  Yitian Xu,et al.  Fault Diagnosis System Based on Rough Set Theory and Support Vector Machine , 2005, FSKD.

[11]  P. Kamath,et al.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts , 2000, Hepatology.

[12]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[13]  Reshma Khemchandani,et al.  Optimal kernel selection in twin support vector machines , 2009, Optim. Lett..

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

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[17]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[18]  Jieping Ye,et al.  A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[20]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[21]  Emmet B. Keeffe,et al.  Minimal criteria for placement of adults on the liver transplant waiting list: A report of a national conference organized by the American Society of Transplant Physicians and the American Association for the Study of Liver Diseases , 1997 .

[22]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[23]  Y. Chawla,et al.  Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific Association for the Study of the Liver (APASL) 2014 , 2009, Hepatology International.

[24]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[25]  Rui Guo,et al.  A twin hyper-sphere multi-class classification support vector machine , 2014, J. Intell. Fuzzy Syst..

[26]  Chunxia Zhao,et al.  Least squares twin support vector machine classification via maximum one-class within class variance , 2012, Optim. Methods Softw..

[27]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[28]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[29]  Mikhail Belkin,et al.  Laplacian Support Vector Machines Trained in the Primal , 2009, J. Mach. Learn. Res..

[30]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[31]  Edward Y. Chang,et al.  Class-Boundary Alignment for Imbalanced Dataset Learning , 2003 .