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

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. General Terms Feature selection; supervised learning

[1]  Dana Kulic,et al.  Feature-Selected Tree-Based Classification , 2013, IEEE Transactions on Cybernetics.

[2]  GuoQiang.  An Effective Algorithm for Improving the Performance of Naive Bayes for Text Classification , 2010 .

[3]  Peter W. Tse,et al.  Anomaly Detection Through a Bayesian Support Vector Machine , 2010, IEEE Transactions on Reliability.

[4]  K. Raghuveer,et al.  Intrusion detection technique by using k-means, fuzzy neural network and SVM classifiers , 2013, 2013 International Conference on Computer Communication and Informatics.

[5]  W. N. H. W. Mohamed,et al.  A comparative study of Reduced Error Pruning method in decision tree algorithms , 2012, 2012 IEEE International Conference on Control System, Computing and Engineering.

[6]  Jyoti Singhai,et al.  Sequential minimal optimization for support vector machine with feature selection in breast cancer diagnosis , 2013, 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013).

[7]  Hongwei Xie,et al.  Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine , 2013, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[8]  Taghi M. Khoshgoftaar,et al.  Stability of Filter- and Wrapper-Based Feature Subset Selection , 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.

[9]  P. R. Devale,et al.  Decision tree based Support Vector Machine for Intrusion Detection , 2010, 2010 International Conference on Networking and Information Technology.

[10]  V. Menkovski,et al.  Oblique Decision Trees using embedded Support Vector Machines in classifier ensembles , 2008, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems.

[11]  Woraphon Lilakiatsakun,et al.  Computer network security based on Support Vector Machine approach , 2011, 2011 11th International Conference on Control, Automation and Systems.

[12]  Lei Li,et al.  Naive Bayes classification algorithm based on small sample set , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[13]  Guo Qiang.  An Effective Algorithm for Improving the Performance of Naive Bayes for Text Classification , 2010, 2010 Second International Conference on Computer Research and Development.

[14]  Neelam Sharma,et al.  INTRUSION DETECTION USING NAIVE BAYES CLASSIFIER WITH FEATURE REDUCTION , 2012 .

[15]  Wilker Altidor,et al.  An Empirical Study on Wrapper-Based Feature Ranking , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[16]  Aboul Ella Hassanien,et al.  Genetic algorithm with different feature selection techniques for anomaly detectors generation , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[17]  Changjing Shang,et al.  Support vector machine-based classification of rock texture images aided by efficient feature selection , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[18]  Shengyan Zhou,et al.  Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Lawrence O. Hall,et al.  Increased classification accuracy and speedup through pair-wise feature selection for support vector machines , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[20]  T. Theeramunkong,et al.  A corpus-based approach for keyword identification using supervised learning techniques , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.