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IFSS – An Improved Filter-Wrapper Algorithm for Feature Subset Selection

by Saurabh Soni, Pratik Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 14
Year of Publication: 2014
Authors: Saurabh Soni, Pratik Patel
10.5120/16665-6656

Saurabh Soni, Pratik Patel . IFSS – An Improved Filter-Wrapper Algorithm for Feature Subset Selection. International Journal of Computer Applications. 95, 14 ( June 2014), 33-35. DOI=10.5120/16665-6656

@article{ 10.5120/16665-6656,
author = { Saurabh Soni, Pratik Patel },
title = { IFSS – An Improved Filter-Wrapper Algorithm for Feature Subset Selection },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 14 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 33-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number14/16665-6656/ },
doi = { 10.5120/16665-6656 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:19:28.702043+05:30
%A Saurabh Soni
%A Pratik Patel
%T IFSS – An Improved Filter-Wrapper Algorithm for Feature Subset Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 14
%P 33-35
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The ever increasing growth of databases in the real time application is a major issue for the handling of large data. The data mining of the same is also a tedious task. The feature subset selection is a process for finding the irrelevant and redundant data and handling them. The proposed algorithm IFSS- Improved Feature Subset Selection works in 2 major steps: 1. Find the irrelevant features and 2. Evaluate its fitness with Ant Colony Optimization (ACO). The Computation time taken to derive the results is taken to compare with different FSS algorithms.

References
  1. Forman G. , An extensive empirical study of feature selection metrics for text classification, Journal of Machine Learning Research, 3, pp 1289-1305, 2003.
  2. Hall M. A. , Correlation-Based Feature Selection for Discrete and Numeric Class Machine Learning, In Proceedings of 17th International Conference on Machine Learning, pp 359-366, 2000.
  3. Scherf M. and Brauer W. , Feature Selection By Means of a Feature Weighting Approach, Technical Report FKI-221-97, Institut fur Informatik, Technische Universitat Munchen, 1997.
  4. Hall M. A. , Correlation-Based Feature Subset Selection for Machine Learning, Ph. D. dissertation Waikato, New Zealand: Univ. Waikato, 1999.
  5. Yu L. and Liu H. , Feature selection for high-dimensional data: a fast correlation-based filter solution, in Proceedings of 20th International Conference on Machine Leaning, 20(2), pp 856-863, 2003.
  6. A Two-phase Feature Selection Method using both Filter and Wrapper. By Huang Yuan, Shian-Shyong Tseng, Wu Gangshan, Zhang Fuya, 1999, IEEE.
  7. Hybrid wrapper-filter approaches for input feature selection using Maximum Relevance and Artificial Neural Network Input Gain Measurement Approximation, by Shamsul Huda, John Yearwood, Andrew Strainieri, 2011 IEEE.
  8. A Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data, by Qinbao Song, Jingjie Ni and Guangtao Wang, 2013, IEEE.
  9. A Survey of Stability Analysis of Feature Subset Selection Techniques. By Taghi M. Khoshgoftaar, Alireza Fazelpour, Huanjing Wang and Randall Wald, 2013, IEEE.
  10. Hall M. A. and Smith L. A. , Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper, In Proceedings of the Twelfth international Florida Artificial intelligence Research Society Conference, pp 235-239, 1999.
  11. Yu L. and Liu H. , Feature selection for high-dimensional data: a fast correlation-based filter solution, in Proceedings of 20th International Conference on Machine Leaning, 20(2), pp 856-863, 2003.
  12. Yu L. and Liu H. , Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, 10(5), pp 1205-1224, 2004.
  13. Zhao Z. and Liu H. , Searching for interacting features, In Proceedings of the 20th International Joint Conference on AI, 2007.
  14. Zhao Z. and Liu H. , Searching for Interacting Features in Subset Selection, Journal Intelligent Data Analysis, 13(2), pp 207-228, 2009.
  15. Quinlan J. R. , C4. 5: Programs for Machine Learning. San Mateo, Calif: Morgan Kaufman, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

FSS filter wrapper ACO IFSS

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