Ensembling Imbalanced-Spatial-Structured Support Vector Machine

Abstract The support vector machine (SVM) and its extensions have been widely used in various areas. However, these methods cannot effectively handle imbalanced data with spatial association. The ensembling imbalanced-spatial-structured support vector machine (EISS-SVM) method is proposed to handle such data. Not only the proposed method accommodates the relationship between the response and predictors, but also accounts for the spatial correlation existing in data which may be imbalanced. The EISS-SVM classifier embraces the usual SVM as a special case. Numerical studies show satisfactory performance of the proposed method, and the analysis results are reported for the application of the proposed method to handling the imaging data from an ongoing prostate cancer research conducted in Canada.

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

[2]  Ting-ting Bi,et al.  Imbalanced Data SVM Classification Method Based on Cluster Boundary Sampling and DT-KNN Pruning , 2014 .

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

[4]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[5]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

[6]  S. Sathiya Keerthi,et al.  Developing parallel sequential minimal optimization for fast training support vector machine , 2006, Neurocomputing.

[7]  Martial Hebert,et al.  Discriminative Random Fields , 2006, International Journal of Computer Vision.

[8]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[10]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[11]  Cecilio Angulo,et al.  A Note on the Bias in SVMs for Multiclassification , 2008, IEEE Transactions on Neural Networks.

[12]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[13]  Abdelhak M. Zoubir,et al.  Bootstrap-based SVM aggregation for class imbalance problems , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[14]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[15]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[16]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[17]  Xiangji Huang,et al.  Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles , 2006, PAKDD.

[18]  Mark W. Schmidt,et al.  Support Vector Random Fields for Spatial Classification , 2005, PKDD.

[19]  Haydemar Núñez,et al.  Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias , 2017, J. Classif..

[20]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[21]  Seyda Ertekin,et al.  Adaptive Oversampling for Imbalanced Data Classification , 2013, ISCIS.

[22]  Haydemar Núñez,et al.  GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems , 2014, Appl. Soft Comput..

[23]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[24]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[25]  Francisco Herrera,et al.  An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..