K-nearest neighbor based bagging SVM pruning

A k-nearest neighbor (kNN) based bagging pruning algorithm for ensemble SVM classification is proposed in this paper. Redundant bags are discarded without reducing the performance of the ensemble SVM classifier. Ten VCI binary classification datasets are used to evaluate the performance of the proposed pruning algorithm against single SVM and bagging SVM classifiers. Results show that the proposed bagging SVM pruning improves the classification accuracies on most of the datasets with use less number of base classifiers thereby reducing computational requirements.

[1]  Li Rong,et al.  Diagnosis of Breast Tumor Using SVM-KNN Classifier , 2010, 2010 Second WRI Global Congress on Intelligent Systems.

[2]  Xingquan Zhu,et al.  A lazy bagging approach to classification , 2008, Pattern Recognit..

[3]  Giorgio Valentini,et al.  Low Bias Bagged Support Vector Machines , 2003, ICML.

[4]  Quan Pan,et al.  Prediction of Protein-RNA interaction site using SVM-KNN algorithm with spatial information , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[6]  Xuesong Wang,et al.  An ensemble SVM using entropy-based attribute selection , 2010, 2010 Chinese Control and Decision Conference.

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

[8]  Gonzalo Martínez-Muñoz,et al.  Using boosting to prune bagging ensembles , 2007, Pattern Recognit. Lett..

[9]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[10]  Yaping Lin,et al.  Gene expression data classification using SVM-KNN classifier , 2004, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004..

[11]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[12]  Kin Keung Lai,et al.  Investigation of Diversity Strategies in SVM Ensemble Learning , 2008, 2008 Fourth International Conference on Natural Computation.

[13]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[15]  Hai Jin,et al.  MSVM-kNN: Combining SVM and k-NN for Multi-class Text Classification , 2008, IEEE International Workshop on Semantic Computing and Systems.

[16]  Qinghua Hu,et al.  Margin distribution based bagging pruning , 2012, Neurocomputing.

[17]  Faisal Zaman,et al.  Double SVMBagging: A New Double Bagging with Support Vector Machine , 2009, Eng. Lett..

[18]  Zhizhong Mao,et al.  Bagging ensemble of SVM based on negative correlation learning , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.