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

MFSPFA: An Enhanced Filter based Feature Selection Algorithm

by V. Arul Kumar, L. Arockiam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 12
Year of Publication: 2012
Authors: V. Arul Kumar, L. Arockiam
10.5120/8096-1682

V. Arul Kumar, L. Arockiam . MFSPFA: An Enhanced Filter based Feature Selection Algorithm. International Journal of Computer Applications. 51, 12 ( August 2012), 27-31. DOI=10.5120/8096-1682

@article{ 10.5120/8096-1682,
author = { V. Arul Kumar, L. Arockiam },
title = { MFSPFA: An Enhanced Filter based Feature Selection Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 12 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number12/8096-1682/ },
doi = { 10.5120/8096-1682 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:50:14.269314+05:30
%A V. Arul Kumar
%A L. Arockiam
%T MFSPFA: An Enhanced Filter based Feature Selection Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 12
%P 27-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature Selection is the process of selecting the momentous feature subset from the original ones. This technique is frequently used as a preprocessing technique in data mining. In this study, a new feature selection algorithm is proposed and is called Modified Fisher Score Principal Feature Analysis (MFSPFA). The new algorithm is developed by combining the proposed Modified Fisher Score (MFS) and Principal Feature Analysis (PFA). The proposed algorithm is tested on publicly available datasets. The experimental results show that, the proposed algorithm is able to reduce the futile features and improves the classification accuracy.

References
  1. Boyang Li,Qiangwei Wang,Jinglu Hua, Feature Subset Selection: A Correlation -Based SVM Filter Approach, IEEJ Transactions On Electrical And Electronic Engineering, Volume 6, 2011,pp. 173-179.
  2. L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt. Feature selection via dependence maximization, Journal of Machine Learning Research, Volume 13, 2012, pp. 1393-1434.
  3. J. Weston, A. Elisseff, B. Schoelkopf, and M. Tipping, Use of the zero norm with linear models and kernel methods, Journal of Machine Learning Research, Volume 3, 2003, pp. 1439–1461.
  4. Julia Handl, Joshua Knowles, Feature Subset Selection in Unsupervised Learning via Multiobjective Optimization, International Journal of Computational Intelligence Research, Volume 2, Number3, 2006, pp. 217–238, ISSN:0973-1873.
  5. Duy-Dinh Le, Shin'ichi Satoh1, An Efficient Feature Selection Method for Object Detection, Springer LNCS, Volume 3686, 2005, pp. 461–468.
  6. Gauthier Doquire, Michel Verleysen, Graph Laplacian for Semi-supervised Feature Selection in Regression Problems, Springer LNCS, Volume 6691, 2011, pp. 248–255.
  7. Jidong Zhaoa,Ke Lua, Xiaofei He, Locality sensitive semi-supervised feature selection, Journal of Neurocomputing, Volume 71, 2008, pp. 1842-1849.
  8. Novi Quadrianto, Alex J. Smola, Tib´erio S. Caetano, Quoc V. Le, Estimating Labels from Label Proportions, Journal of Machine Learning Research, Volume 10, 2009, pp. 2349-2374.
  9. Li juan Wang, An improved multiple fuzzy NNC system based on mutual information and fuzzy integral, International Journal of Machine Learning and Cybernetics, Volume 2, Number 1, 2011, pp. 25-36.
  10. Yuxuan SUN, Xiaojun LOU, Bisai BAO, A Novel Relief Feature Selection Algorithm Based on Mean-Variance Model, Journal of Information & Computational Science, Volume 8, Number 16, 2011, pp. 3921–3929.
  11. Xiaofei He, Ming Ji, Chiyuan Zhang, Hujun Bao, A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 33, Number 10, October 2011, pp. 2013-2025.
  12. Jian-Bo Yang, Kai-Quan Shen,Chong-Jin Ong,Xiao-Ping Li, Feature Selection for MLP Neural Network: The Use of Random Permutation of Probabilistic Outputs, IEEE Transactions on Neural Networks, Volume 20, Issue 12, December 2009,pp. 1911-1922.
  13. Zheng Zhao, Huan Liu, Spectral feature selection for supervised and unsupervised learning, Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 1151-1157, ISBN: 978-1-59593-793-3.
  14. P. Daniusis,P. Vaitkus, Supervised Feature Extraction Using Hilbert-Schmidt Norms, Springer LNCS, Volume 5788, 2009, pp. 25-33.
  15. Feiping Nie, Shiming Xiang,Yangqing Jia, Changshui Zhang, Shuicheng Yan, Trace ratio criterion for feature selection, 23rd National Conference on Artificial Intelligence, Volume 2, 2008, pp. 671-676, ISBN: 978-1-57735-368-3
  16. P. E. H. R. O. Duda and D. G. Stork, Pattern Classification. Wiley-Interscience Publication, 2001.
  17. X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, Advances in Neural Information Processing Systems, Volume 18, 2005, pp. 507-514, ISBN : 978-0-262-23253-1
  18. A. Gretton, O. Bousquet, A. Smola, B. Schoelkopf, Measuring Statistical Dependence with Hilbert-Schmidt Norms, Proceding of the 16th International Conference Algorithmic Learning Theory, 2005, pp. 63-78. DOI:270036
  19. Fengxi Song, Zhongwei Guo,Dayong Mei, Feature Selection Using Principal Component Analysis, IEEE International Conference onSystem Science, Engineering Design and Manufacturing Informatization, Volume 1, 2010, pp. 27-30.
  20. Yijuan Lu, Ira Cohen, Xiang Sean Zhou, Qi Tian, Feature
Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Modified Fisher Score Principal Component Analysis Principal Feature Analysis

Powered by PhDFocusTM