We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Data Mining and Soft Computing using Support Vector Machine: A Survey

by Subhankar Das, Sanjib Saha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 14
Year of Publication: 2013
Authors: Subhankar Das, Sanjib Saha
10.5120/13554-1367

Subhankar Das, Sanjib Saha . Data Mining and Soft Computing using Support Vector Machine: A Survey. International Journal of Computer Applications. 77, 14 ( September 2013), 40-47. DOI=10.5120/13554-1367

@article{ 10.5120/13554-1367,
author = { Subhankar Das, Sanjib Saha },
title = { Data Mining and Soft Computing using Support Vector Machine: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 14 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 40-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number14/13554-1367/ },
doi = { 10.5120/13554-1367 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:48:50.967983+05:30
%A Subhankar Das
%A Sanjib Saha
%T Data Mining and Soft Computing using Support Vector Machine: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 14
%P 40-47
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the basic concepts and survey of the available literature on Support Vector Machines (SVM) in data mining and soft computing research area is provided. While at the time of survey several new methods were found related to SVM like as Support Vector Representation and Discrimination Machine (SVRDM), Recursive SVM (RSVM), On-line Independent SVM (OISVM), Pruning SVM, Fast Nearest Neighbor Condensation classifier (FCNN-SVM), Improved SV Clustering (iSVC), Cost-sensitive SVM (2v-SVM), 2C-SVM, Profile SVM (PSVM), Twin SVM (TWSVM), Twin Bounded SVM (TBSVM), Parametric-margin n-SVM (par-n-SVM), Twin Parametric-Margin SVM (TPMSVM), Structural Twin SVM (S-TWSVM), Hierarchical Linear SVM (H-LSVM), Bio-SVM, FuzzySVM-CIL, Kernel Fuzzy C-Means clustering-based Fuzzy SVM (KFCM-FSVM), Multi-Class Instance Selection (MCIS). After studied these methods a comparative and analytical survey upon those methods are presented here. Also a large future scope is available on several techniques and they are discussed in this paper.

References
  1. V. N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, 1995.
  2. Jiawei Han, Micheline Kember, Jian Pei, "Data Mining Concepts and Techniques", 3rd Edition, Morgan Kaufmann, 2012.
  3. Yu-Chiang Frank Wang, David Casasent, "New support vector-based design method for binary hierarchical classifiers for multi-class classification problems" Neural Networks 21 (2008) 502–510, 2008 Special Issue.
  4. Qing Tao, Dejun Chu, Jue Wang, "Recursive Support Vector Machines for Dimensionality Reduction" IEEE Transactions on Neural Networks, Vol. 19, No. 1, January 2008.
  5. Pei-Yi Hao, "New support vector algorithms with parametric insensitive/margin model", Neural Networks 23 (2010) 60-73.
  6. Minh Hoai Nguyen, Fernando de la Torre, "Optimal feature selection for support vector machines", Pattern Recognition 43 (2010) 584–591.
  7. Francesco Orabona, Claudio Castellini, Barbara Caputo, LuoJie, Giulio Sandini, "On-line independent support Vector machines', Pattern Recognition 43 (2010) 1402-1412, ScienceDirect.
  8. Xun Liang, Senior Member, IEEE, "An Effective Method of Pruning Support Vector Machine Classifiers", IEEE Transactions on Neural Networks, Vol. 21, No. 1, January 2010.
  9. Fabrizio Angiulli, Annabella Astorino, "Scaling Up Support Vector Machines Using Nearest Neighbor Condensation", IEEE Transactions on Neural Networks, Vol. 21, No. 2, February 2010.
  10. Chong-Jin Ong, Shiyun Shao, Jianbo Yang, "An Improved Algorithm for the Solution of the Regularization Path of Support Vector Machine", IEEE Transactions on Neural Networks, Vol. 21, No. 3, March 2010.
  11. Ling Ping, Zhou Chun-Guang, Zhou Xu, "Improved support vector clustering", Engineering Applications of Artificial Intelligence 23 (2010) 552–559, ScienceDirect.
  12. Haibin Cheng, Pang-Ning Tan, Member, IEEE, and Rong Jin, Member, IEEE," Efficient Algorithm for Localized Support Vector Machine", "IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 4, April 2010.
  13. Masayuki Karasuyama, Student Member, IEEE, and Ichiro Takeuchi, Member, IEEE, "Multiple Incremental Decremental Learning of Support Vector Machines", IEEE Transactions on Neural Networks, Vol. 21, No. 7, July 2010.
  14. Mark A. Davenport, Student Member, IEEE, Richard G. Baraniuk, Fellow, IEEE, and Clayton D. Scott, Member, IEEE, "Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 10, October 2010.
  15. Ricardo Santiago-Mozos, Member, IEEE, Fernando Pérez Cruz, Senior Member, IEEE, and Antonio Artés-Rodríguez, Senior Member, IEEE, "Extended Input Space Support Vector Machine", IEEE Transactions on Neural Networks, Vol. 22, No. 1, January 2011.
  16. Guillermo L. Grinblat, Lucas C. Uzal, H. Alejandro Ceccatto, and Pablo M. Granitto, "Solving Nonstationary Classification Problems with Coupled Support Vector Machines", IEEE Transactions on Neural Networks, Vol. 22, No. 1, January 2011.
  17. Xinjun Peng, "TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition", Pattern Recognition 44 (2011) 2678–2692, ScienceDirect.
  18. Jian-Bo Yang and Chong-Jin Ong, "Determination of Global Minima of Some Common Validation Functions in Support Vector Machine", IEEE Transactions on Neural Networks, Vol. 22, No. 4, April 2011.
  19. HuiXue, Songcan Chen, and Qiang Yang, Fellow, IEEE, "Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier", IEEE Transactions on Neural Networks, Vol. 22, No. 4, April 2011.
  20. GaoJuna, Fu-lai Chung, Wang Shitong, "Matrix pattern based minimum within-class scatter support vector machines", Applied Soft Computing 11 (2011) 5602–5610, ScienceDirect.
  21. Yuan-Hai Shao, Chun-Hua Zhang, Xiao-Bo Wang, and Nai-Yang Deng, "Improvements on Twin Support Vector Machines", IEEE Transactions on Neural Networks, Vol. 22, No. 6, June 2011.
  22. Keng-Pei Lin and Ming-Syan Chen, Fellow, IEEE, "On the Design and Analysis of the Privacy-Preserving SVM Classifier", IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 11, November 2011.
  23. Bohyung Han, Member, IEEE, and Larry S. Davis, Fellow, IEEE, "Density-Based Multifeature Background Subtraction with Support Vector Machine", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 5, May 2012.
  24. Irene Rodriguez-Lujan, Carlos Santa Cruz, Ramon Huerta, "Hierarchical linear support vector machine", Pattern Recognition 45 (2012) 4414–4427, SciVerse ScienceDirect.
  25. You Ji, Shiliang Sun, "Multitask multiclass support vector machines: Model and experiments", Pattern Recognition 46 (2013) 914–924, SciVerse ScienceDirect.
  26. Jian-Xun Peng, Stuart Ferguson, Karen Rafferty, Victoria Stewart, "A sequential algorithm for sparse support vector classifiers", Pattern Recognition 46 (2013) 1195–1208, SciVerse ScienceDirect.
  27. Subhransu Maji, Member, IEEE, Alexander C. Berg, Member, IEEE, and Jitendra Malik, Fellow, IEEE, "Efficient Classification for Additive Kernel SVMs", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 1, January 2013.
  28. Essam Al Daoud, Hamza Turabieh, "New empirical nonparametric kernels for support vector machine classification", Applied Soft Computing 13 (2013) 1759-1765, SciVerse ScienceDirect.
  29. Zhiquan Qi, Yingjie Tian, Yong Shi, "Structural twin support vector machine for classification", Knowledge Based Systems 43 (2013) 74–81, SciVerse ScienceDirect.
  30. Jingnian Chen, Caiming Zhang, Xiaoping Xue, Cheng-Lin Liu, "Fast instance selection for speeding up support vector machines", Knowledge-Based Systems 45 (2013) 1–7, SciVerse ScienceDirect.
  31. S. Rajasekaran, G. A. Vijayalaksmi Pai, "Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications", Prentice Hall, 2005.
  32. Manuele Bicegoa, Mario A. T. Figueiredob, "Soft clustering using weighted one-class support vector machines", Pattern Recognition 42 (2009) 27 – 32, ScienceDirect.
  33. Magnus Jandel, "A neural support vector machine", Neural Networks 23 (2010) 607_613, ScienceDirect.
  34. Sangjun Lee, Changyi Park, Ja-Yong Koo, "Feature selection in the Laplacian support vector machine", Computational Statistics and Data Analysis 55 (2011) 567-577, ScienceDirect.
  35. Rukshan Batuwita and Vasile Palade, "FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning", IEEE Transactions on Fuzzy Systems, Vol. 18, No. 3, June 2010.
  36. Xiaowei Yang, Guangquan Zhang, Jie Lu, Member, IEEE, and Jun Ma, "A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises", IEEE Transactions on Fuzzy Systems, Vol. 19, No. 1, February 2011.
  37. Pengfei Zhu, Qinghua Hu, "Rule extraction from support vector machines based on consistent region covering reduction", Knowledge-Based Systems 42 (2013) 1–8, SciVerse ScienceDirect.
  38. R P Datta, Sanjib Saha, "An Empirical comparison of rule based classification techniques in medical databases", Working paper, Indian Institute of Foreign Trade (IIFT), India, August, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Soft Computing SVM Maximum Margin Soft-Margin Kernel Trick.