Performance Assessment of Machine Learning Algorithms with Feature Selection Methods
Keywords:
Machine Learning, Logistic Regression, Naïve Bayes, Random Forest, Gain Ratio, Information GainAbstract
Machine learning is a field of artificial intelligence in which computers learn from experience. The field of machine learning is a famous research area in computer science. Machine learning applications are helpful in various domains of computer science, chemical sciences, spatial technology, bioinformatics, agriculture, digital forensics and more. Machine learning algorithms are useful in the fields of pattern recognition, pattern classification, text classification, SMS classification, computer vision, mobile learning and more. In the present work performance assessment of three machine learning algorithms namely logistic regression, random forest and naïve bayes with three feature selection methods viz. correlation based, Information based and gain ratio is conducted on a mobile device. The above-mentioned machine learning algorithms along with feature selection methods are assessed for the performance metrics of accuracy, precision, F- Measure, recall and Receiver Operating Characteristics.
References
Ethem Alpaydin. Introduction to Machine Learning, Second Edition, MIT press, Cambridge, London, 2009.
E. Short, D.F.Seifer, M. Matari, “A Comparison of Machine Learning Techniques for Modeling Human-Robot Interaction with Children with Autism”, Human-Robot Interaction (HRI), 6th ACM/IEEE International conference, 251 – 252, Lausanne, 2011.
R. Zhang, A.Bivens, “Comparing the Use of Bayesian Networks and Neural Networks in Response Time Modeling for Service- oriented Systems”, In Proceedings of the 2007 workshop on Service-oriented computing performance: aspects, issues, and approaches, pp.67 – 74, New York, USA, 2007.
D. Han, J. Zhang, “ A Comparison of Two Algorithms for Predicting the Condition Number”, Sixth International Conference on Machine Learning and Applications, 13-15 Dec. pp.223 – 228, Cincinnati, OH, 2007.
G. Sikka, A. Kaur, M. Uddin, “Estimating Function points: Using Machine Learning and Regression Models”, 2nd International Conference on Education Technology and Computer (ICETC), Shanghai, 2010.
A. Bashar, G. Parr, S. McClean, B. Scotney, D. Nauck, “Machine Learning based Call Admission Control Approaches: A Comparative Study”, IEEE International Conference on Network and Service Management (CNSM), pp.431–434, Niagara Falls, ON, 2010.
M. Bahrololum, E. Salahi, M. Khaleghi, “Machine Learning Techniques for feature Reduction in Intrusion Detection Systems: A Comparison”, Fourth International Conference on Computer Sciences and Convergence Information Technology, pp.1091–1095, Seoul, 2009.
J. Alonso, L. Belanche, D.R. Avresky, “Predicting Software Anomalies using Machine Learning Techniques”, IEEE International Symposium on Network Computing and Applications (NCA), pp.163–170, Cambridge, MA, 2011.
L. Vanajakshi, L. Rillet, “Support Vector Machine Technique for the Short Term Prediction of Travel Time”, IEEE International Symposium on Intelligent Vehicles, pp.600–605, Istanbul, 2007.
R. Eisinger, R. Romero, R. Goularte, “Machine Learning Techniques Applied to Dynamic Video Adapting”, IEEE Seventh International conference on Machine Learning and Applications (ICMLA '08), 2008.
A. Forster, “Machine Learning Techniques Applied to Wireless Ad-Hoc Networks: Guide and Survey”, International conference on Intelligent Sensors, Sensor Networks and Information, pp. 365 – 370, Melbourne, Queensland, 2007.
M. Slavik, I. Mahgoub, “Applying Machine Learning to the Design of Multi-Hop Broadcast Protocols for VANET”, Seventh International Conference on Wireless Communications and Mobile Computing (IWCMC), pp.1742–1747, 2011.
M. Grajzer, M. Koziuk, P. Szczechowiak, A. Pescap, “A Multi-Classification Approach for the Detection and Identification of eHealth Applications”, Twenty First International conference on Computer Communications and Networks (ICCCN), pp. 1 – 6, Munich, 2012.
D. Weir, S. Rogers, R. Murray-Smith, M. Lochtefeld, “A User-Specific Machine Learning Approach for Improving Touch Accuracy on Mobile Devices”, UIST, Cambridge, Massachusetts, USA, 2012.
M.A. Hall, “Correlation-Based Feature Subset Selection for Machine Learning”, University of Waikato, Hamilton, 1999.
A. Sharma, S. Dey, “Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis features”, Special Issue of International Journal of Computer Applications (0975 – 8887) on Advanced Computingand Communication Technologies for HPC Applications, 2012.
M. Sokolova, G. Lapalme, “A systematic analysis of performance measures for classification tasks”, Information Processing and Management, Vol.45, pp.427–437, 2009.
P. Liu, Y. Chen, W. Tang, Q. Yue, “Mobile WEKA as Data Mining Tool on Android”, Advances in Intelligent and Soft Computing Vol.139, pp.75-80, 2012.
L. Silva, M. Koga, C. Cugnasca, A. Cost Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedling”, Computers and Electronics in Agriculture, Vol.97, pp.47–55, 2013.
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