CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Formulation of Feature Selection with Support Vector Machine

by Gend Lal Prajapati, Arti Patle
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 5
Year of Publication: 2015
Authors: Gend Lal Prajapati, Arti Patle
10.5120/ijca2015905325

Gend Lal Prajapati, Arti Patle . Formulation of Feature Selection with Support Vector Machine. International Journal of Computer Applications. 123, 5 ( August 2015), 20-25. DOI=10.5120/ijca2015905325

@article{ 10.5120/ijca2015905325,
author = { Gend Lal Prajapati, Arti Patle },
title = { Formulation of Feature Selection with Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 5 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number5/21956-2015905325/ },
doi = { 10.5120/ijca2015905325 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:52.357930+05:30
%A Gend Lal Prajapati
%A Arti Patle
%T Formulation of Feature Selection with Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 5
%P 20-25
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Basic question arises when classification came in picture classification accuracy, ensemble size, and computational complexity. Feature selection is importance for improvement and performance of classification algorithm. Classification algorithm may not scale up to the size of the full feature set either in sample or time but with feature selection help us to better understand the domain with Cheaper to collect a subset of predictors and Safer to collect a reduced subset of predictors. An important pre-processing step in classification tasks is called as, Feature selection its aims to minimize both the classification error rate and the number of features for inference knowledge in any domain. Feature selection is Minimum set F that achieves maximum classification performance of T (for a given set of classifiers and classification performance metrics). This paper proposes feature selection methodology which includes ranking, information gain and filter method concept. After the feature subset train SVM with RBF kernel for classification.

References
  1. H. Liu and H. Motoda. 1998. “Feature Selection for Knowledge Discovery and Data Mining”, Kluwer Academic, Boston.
  2. J. X. Dong, A. Krzyzak and C. Suen. 1998. “Fast SVM training algorithm with decomposition on very large datasets”, IEEE Transaction Pattern Analysis Mach. Intell., Vol. 27 (4), pp. 603 –618.
  3. Y. Liu and F. Zheng. 2006. “A novel feature selection method for support vector machines”, Pattern Recognition Society, Published by Elsevier Ltd, vol. 39, pp. 1333-1345.
  4. H. Liu, Zhao and Zheng. 2007. “Searching for Interacting Features”, IJCAI, vol. 7, pp. 1156-1161.
  5. D. Morariu, L. Vintan, and V. Tresp. 2008. “Feature Selection Methods for an Improved SVM Classifier”, World Academy Science, Engineering and Technology, vol. 2, 2008.
  6. M. Fatih Akay. 2009. Support vector machines combined with feature selection for Breast cancer diagnosis, Expert Systems with Applications An International Journal, Volume 36 (2), pp. 3240-3247.
  7. B. Waske, S. Linden and J. Benediktsson. 2010. “Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data”, IEEE Transactions on Geoscience And Remote Sensing, Vol. 48 (7), pp. 2880 – 2889.
  8. S. Ding and Li Chen. 2010. “Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines”, Intelligent Information Management, vol. 2, pp. 159-169.
  9. G. L. Prajapati and A. Patle. 2010. “On Performing Classification Using SVM with Radial Basis and Polynomial Kernel Functions”, ICETET-2010 3rd International IEEE Conference, pp. 512 – 515.
  10. H. Minh and F. Torre. 2010. Optimal Feature Selection for Support Vector Machines, Robotics Institute, Carnegie Mellon University USA, vol. 43(3), pp. 584-591.
  11. G. Wang and Liu. 2011. “A Hybrid Feature Selection Method for Data Sets of thousands of Variables”, Advance Computer Control (ICACC), vol.2, pp. 288-291.
  12. Q. Liu, C. Chen and Y. Zhang. 2011. “Feature selection for support vector machines with RBF kernel”, Artificial Intelligent Review, vol. 36, pp. 99–115.
  13. E. Karabulut, S. Özel and T. İbrikçi. 2012. “A Comparative Study on The Effect of Feature Selection on Classification Accuracy”, First World Conference on Innovation and Computer Sciences, Vol. 1, pp. 323–327.
  14. Y. Jeong, I. Kang and M. Jeong. 2012. “A New Feature Selection Method for One-Class Classification Problems”, IEEE TRANSACTIONS on Systems, Man and Cybernetics—part C: Applications And Reviews, Vol. 42, No. 6, pp. 103-114.
  15. J. Tang, S. Alelyani and H. Liu, “Feature Selection for Classification: A Review” http://core.ac.uk/download/pdf/23799612.pdf, accessed on: 06 May 2015.
  16. S. Deng, Y. Xu and L. Xiaoli. 2013. “A Feature-Selection Algorithm Based On Support Vector Machine-Multiclass For Hyperspectral Visible Spectral Analysis”, Journal of Food Engineering, Vol. 119 (1), pp. 159–166.
  17. G. Wang, Song, Qinbao and J. Ni. 2013. “A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data”, Knowledge and Data Engineering, IEEE Transactions, vol. 25, pp. 1-14.
  18. G. L. Prajapati and A. Patle. 2014. “Effective Classification with Dimension Reduction on Support Vector Machine”, International Journal of Science, Technology and Management, Vol. 6, pp. 1-13.
  19. P. Ghamisi. 2015. “A Novel Feature Selection Approach Based on FODPSO and SVM”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53(5), pp. 2935 – 2947.
  20. Machine Learning Library, center for Machine Learning and Intelligent System, http://archive.ics.uci.edu/ml/ accessed on: April 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Ranking Feature classification kernel filter information.

Powered by PhDFocusTM