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

Feature Selection based Classification using Naive Bayes, J48 and Support Vector Machine

by Dipali Bhosale, Roshani Ade, P. R. Deshmukh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 16
Year of Publication: 2014
Authors: Dipali Bhosale, Roshani Ade, P. R. Deshmukh
10.5120/17456-8202

Dipali Bhosale, Roshani Ade, P. R. Deshmukh . Feature Selection based Classification using Naive Bayes, J48 and Support Vector Machine. International Journal of Computer Applications. 99, 16 ( August 2014), 14-18. DOI=10.5120/17456-8202

@article{ 10.5120/17456-8202,
author = { Dipali Bhosale, Roshani Ade, P. R. Deshmukh },
title = { Feature Selection based Classification using Naive Bayes, J48 and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 16 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number16/17456-8202/ },
doi = { 10.5120/17456-8202 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:28:22.142802+05:30
%A Dipali Bhosale
%A Roshani Ade
%A P. R. Deshmukh
%T Feature Selection based Classification using Naive Bayes, J48 and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 16
%P 14-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One way to improve accuracy of a classifier is to use the minimum number of features. Many feature selection techniques are proposed to find out the most important features. In this paper, feature selection methods Co-relation based feature Selection, Wrapper method and Information Gain are used, before applying supervised learning based classification techniques. The results show that Support vector Machine with Information Gain and Wrapper method have the best results as compared to others tested.

References
  1. Shengyan Zhou, Iagnemma K. , "Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines",International Conference on Intelligent Robots and Systems, pp. 1183–1189, IEEE, 2010.
  2. Techo, J. Nattee, C. Theeramunkong, T. , "A Corpus-Based Approach For Keyword Identification Using Supervised Learning Techniques", 5th International Conference On Electrical Engineering/Electronics, Computer, Telecommunications And Information Technology, Ecti-Con, Vol. 1, pp. 33 – 36, 2008.
  3. Cecille Freeman, Dana Kuli´Cand OtmanBasir,"Feature-Selected Tree-Based Classification", IEEE Transactions on Cybernetics, Vol. 43, No. 6, pp. 1990-2004, December 2013.
  4. Wald R. , Khoshgoftaar T. M. , Napolitano A. , "Stability Of Filter- And Wrapper-Based Feature Subset Selection", IEEE 25th International Conference On Tools With Artificial Intelligence, pp. 374 – 380, 2013.
  5. Dr. Saurabh Mukherjee, Neelam Sharma, "Intrusion Detection Using Naive Bayes Classifier With Feature Reduction", Elsevier, pp. 119 – 128, 2012.
  6. Amirasayed A. Aziz, Ahmad Taherazar, Mostafa A. Salamaand Sanaa El-Ola Hanafy, "Genetic Algorithm With Different Feature Selectiontechniques For Anomaly Detectors Generation", IEEE Federated Conference On computer Science And Information Systems, pp. 769–774, 2013.
  7. Altidor W. ,Khoshgoftaar T. M. , Van Hulse J. , "An Empirical Study On Wrapper-Based Feature Ranking", 21st International Conference On tools With Artificial Intelligence, pp. 75-82, IEEE 2009.
  8. Yuguang Huang,Lei Li, "Naive Bayes classification algorithm based on small sample set", IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 34 – 39, 2011.
  9. GuoQiang,"An Effective Algorithm for Improving the Performance of Naive Bayes for Text Classification", IEEE Second International Conference on Computer Research and Development, pp. 699–701, 2010.
  10. Menkovski, V. ,Efremidis, S. , "Oblique Decision Trees using embedded Support Vector Machines in classifier ensembles", 7th IEEE International Conference on Cybernetic Intelligent Systems (CIS), pp. 1-6, 2008.
  11. Mohamed W. N. H. W. , Salleh, M. N. M. , Omar, A. H. ,"A comparative study of Reduced Error Pruning method in decision tree algorithms", IEEE International Conference on Control System, Computing and Engineering, pp. 392 – 397, 2012.
  12. HaoWu, Xianmin Zhang, HongweiXie, Yongcong Kuang, "Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine", IEEE Transactions On Components, Packaging and Manufacturing Technology, Vol. 3, No. 3,pp. 516-522, March 2013.
  13. A. M. Chandrasekhar and K. Raghuveer, "Intrusion Detection Technique by using K-means, Fuzzy Neural Network and SVM classifiers", IEEE International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2013.
  14. Vasilis A. Sotiris, Peter W. Tse, and Michael G. Pecht, "Anomaly Detection Through a Bayesian Support Vector Machine", IEEE Transactions On Reliability, Vol. 59, No. 2,pp. 277-286, June 2010.
  15. PreechaSomwangand Woraphon Lilakiatsakun, "Computer Network Security Based On Support Vector Machine Approach", IEEE 11th International Conference on Control, Automation and Systems, pp. 155-160, 2011.
  16. Mrs. Snehal A. Mulay, Prof. P. R. Devale, "Decision Tree based Support Vector Machine for Intrusion Detection", IEEE International Conference on Networking and Information Technology, pp. 59-63, 2010.
  17. Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall and Andrew Remsen, "Increased Classification Accuracy and Speedup Through Pair-wise Feature Selection for Support Vector Machines", IEEE, 2011.
  18. Ajay Urmaliya, Dr. JyotiSinghai, "Sequential Minimal Optimization for Support Vector Machine with Feature Selection in Breast Cancer Diagnosis", IEEE Second International Conference on Image Information Processing (ICIIP), pp. 481-486, 2013.
  19. Changjing Shang and Dave Barnes, "Support Vector Machine-Based Classification of Rock Texture Images Aided by Efficient Feature Selection", WCCI 2012 IEEE World Congress on Computational Intelligence, June2012.
  20. https://archive. ics. uci. edu/ml/data sets/Iris
  21. http://archive. ics. uci. edu/ml/dataset/Glass+Identification
Index Terms

Computer Science
Information Sciences

Keywords

Naïve Bayes SVM J48 Correlation Feature Selection Information Gain wrapper method

Powered by PhDFocusTM