CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Review on Dimensionality Reduction Techniques

by Priyanka Jindal, Dharmender Kumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 2
Year of Publication: 2017
Authors: Priyanka Jindal, Dharmender Kumar
10.5120/ijca2017915260

Priyanka Jindal, Dharmender Kumar . A Review on Dimensionality Reduction Techniques. International Journal of Computer Applications. 173, 2 ( Sep 2017), 42-46. DOI=10.5120/ijca2017915260

@article{ 10.5120/ijca2017915260,
author = { Priyanka Jindal, Dharmender Kumar },
title = { A Review on Dimensionality Reduction Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 2 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number2/28311-2017915260/ },
doi = { 10.5120/ijca2017915260 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:13.407094+05:30
%A Priyanka Jindal
%A Dharmender Kumar
%T A Review on Dimensionality Reduction Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 2
%P 42-46
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Progress in digital data acquisition and storage technology has resulted in exponential growth in high dimensional data. Removing redundant and irrelevant features from this high-dimensional data helps in improving mining performance and comprehensibility and increasing learning accuracy. Feature selection and feature extraction techniques as a preprocessing step are used for reducing data dimensionality. This paper analyses some existing popular feature selection and feature extraction techniques and addresses benefits and challenges of these algorithms which would be beneficial for beginners..

References
  1. Kashif Javed, Haroon A. Babri and Mehreen Saeed, “Feature Selection based on Class-Dependent Densities for High Dimensional Binary Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 3, pp. 465-477, 2012.
  2. I. Guyon, S. Gunn, A. Ben-Hur and G. Dror, “Feature Selection Challenge”, In Advances in Neural Information Processing Systems Conference (NIPS), MIT Press, pp. 545-552, 2003.
  3. Kajal Naidu, Aparna Dhemge and Kapil Wankhade, “Feature Selection Algorithm for Improving the Performace of Classification: A Survey”, In proceedings of fourth international conference on Communication Systems and Network Technology, pp. 468-471, 2014.
  4. G. K. Janecek, G. F. Gansterer, M. A. Demel, G. F. Ecker “On the Relationship between Feature Selection and Classification Accuracy”, In Proceeding of workshop and conference on New Challenges for Feature Selection, pp. 40-105, 2008.
  5. A. H. Shahana, V. Preeja, “ Survey on Feature Subset Selection for high dimensional data”, In proceedings of International Conference on Circuit, Power and Computing Technologies, pp. 1-4, 2016.
  6. H. Motoda and H. Liu, “Feature Selection, Extraction and Construction”, In proceedings of Conference on Knowledge Discovery and Data Mining, Taipei, Taiwan, pp. 67–72, 2002.
  7. Monica Rogati, Yiming Yang, “High-performing Feature Selection for Text Classification,” In proceedings of 11th International Conference on Information and Knowledge Management, McLean, Virginia, USA, pp.659 – 661, 2002.
  8. Zilin Zeng, Hongjun Zhang, Rui Zhang and Youliang Zhang, “Hybrid Feature Selection Method based on Rough Conditional Mutual Information and Naïve Bayesian Classifier”, ISRN Applied Mathematics, Vol. 2014, Article Id 382738, pp. 1-11, 2014.
  9. K. Sutha, Dr. J. Jebamalar Tamilselvi, “ A Review of Feature Selection Algorithms for Data Mining Techniques”, International Journal of Computer Science and Engineering, Vol. 7, No. 6, pp. 63-67, 2015.
  10. I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, Vol. 3, pp. 1157-1182, 2003.
  11. Jiliang Tang, Salem Alelyani and Huan Liu, “Feature Selection for Classification: A Review”, In Data classification: Algorithms and Applications, pp. 1-37, 2014.
  12. M. Ramaswami and R. Bhaskaran, “A Study on Feature Selection Techniques in Educational Data Mining”, Journal of Computing, Vol. 1, No. 1, pp. 7-11, 2009.
  13. R. Kohavi and G.H. John, “Wrappers for Feature Subset Selection”, Artificial Intelligence, Vol. 97, No. 1-2, pp. 273–324, 1997.
  14. Samina Khalid, Tehmina Khalil and Sharmila Nasreen, “A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning”, In Proceedings of Conference on Science and Information, pp. 372-378, 2014.
  15. Zheng Zhao and Huan Liu “Searching for Interacting Features” In proceedings of 20th International joint Conference on Artificial Intelligence, pp. 1156-1161, 2007.
  16. K.Kira and L.A Rendell, “The Feature Selection Problem: Traditional Methods and A New Algorithm,” In proceedings of 10th National Conference on Artificial Intelligence, pp.129-134, 1992.
  17. Qinbao Song, Jingjie Ni and Guangtao Wang, “A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No.1, pp. 1-14, 2013
  18. Mark A. Hall and Lloyd A. Smith, “Feature Selection for Machine Learning: Comparing a Correlation-based Filter Approach to the Wrapper”, In proceedings of the Twelfth International Conference on Florida Artificial Intelligence Research Society, Vol. 235, pp. 235-239, 1999.
  19. Wang Liping, “Feature Selection Algorithm Based On Conditional Dynamic Mutual Information”, International Journal on Smart Sensing and Intelligent Systems”, Vol. 8, No. 1, pp. 316-337, 2015.
  20. Kexin Zhu and Jian Yang, “A Cluster-Based Sequential Feature Selection Algorithm”, In proceedings of 9th International Conference on Natural Computation, pp. 848-852, 2013.
  21. Y.Kim, W.Street, and F.Menczer, “Feature Selection for Unsupervised Learning Via Evolutionary Search,” In proceedings of 6th International Conference on Knowledge Discovery and Data Mining, pp 365-369, 2000.
  22. B.M Vidhyavathi, “A New Approach to Feature Selection for Data Mining”, International Journal of Computational Intelligence Research, Vol.7, No. 3, pp. 263 – 269, 2011.
  23. Jihong Liu, “A Hybrid Feature Selection Algorithm for Data sets of thousands of Variables” In proceedings on 2nd International Conference on Advanced Computer Control, pp. 288-291, 2010.
  24. Prashant K. Aher, Swapnil D. Daphal and Alice N. Cheeran, “ Analysis of Feature Extraction Techniques for Improved Emotion Recognition in Presence of Additive Noise”, In proceedings of International Conference on Computation System and Information Technology for Sustainable Solutions, pp. 350-354, 2016.
  25. Ali Sophian , Gui Yun Tian, David Taylor and John Rudlin, “A feature Extraction Technique Based on Principal Component Analysis for Pulsed Eddy Current NDT ”, NDT & E International, Vol.36 , No. 1 , pp. 37-41, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Feature Extraction Principal Component Analysis (PCA) Filter methods Wrapper Methods.

Powered by PhDFocusTM