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

by Subhankar Das, Sanjib Saha
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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.

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Index Terms

Computer Science
Information Sciences

Keywords

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

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