Conceptual Review on Machine Learning Algorithms for Classification Techniques

Authors

  • T. Mohana Priya  Assistant Professor, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, Tamil Nadu, India
  • Dr.M.Punithavalli  Professor, Department of Computer Applications, Bharathiar University, Coimbatore, Tamil Nadu, India
  • Dr. R. Rajesh Kanna  Professor and Head, Department of Information Technology, Dr.N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India

Keywords:

Machine Learning, KNN, ANN, Naive Bayes, Classification

Abstract

Machine leaning is a ground of recent research that officially focuses on the theory, performance, and properties of learning systems and algorithms. It is a extremely interdisciplinary field building upon ideas from many different kinds of fields such as artificial intelligence, optimization theory, information theory, statistics, cognitive science, optimal control, and many other disciplines of science, engineering, and mathematics. Because of its implementation in a wide range of applications, machine learning has covered almost every scientific domain, which has brought great impact on the science and society. It has been used on a variety of problems, including recommendation engines, recognition systems, informatics and data mining, and autonomous control systems. This research paper compared different machine algorithms for classification. Classification is used when the desired output is a discrete label.

References

  1. Y. Lertworaprachaya, Y. Yang, R. John, “Interval valued fuzzy decision trees with optimal neighborhood perimeter,” Applied Soft Computing, vol. 24, pp. 851-866, 2014
  2. X. Pan, Y. Luo, Y. Xu, “K-nearest neighbor based structural twin support vector machine,” Knowledge Based Systems, vol. 88, pp. 34-44, 2015
  3. J. Xu, Q. Wu, J. Zhang, Z. Tang, “Exploiting Universum data in AdaBoost using gradient descent,” Image and Vision Computing, vol. 32, pp. 550-557, 2014
  4. Kalai Magal. R, Shomona Gracia Jacob, “Improved Random Forest Algorithm for Software Defect Prediction through Data Mining Techniques”, International Journal of Computer, pp.0975–8887, Vol.No.23, May2015
  5. L Jiang, D Wang, Z Cai, and XYan: Survey of improving naive bayes for classification.Advanced Data Mining and Applications 2017:134-145.
  6. Cover T, Hart P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory. Available from: http://ieee.org/document/1053964/. Accessed in 2017 (Feb 20).
  7. Elder, J. (n.d). Introduction to Machine Learning and Pattern Recognition, IEEE, Available at LASSONDE University EECS Department York website: http://www.eecs.yorku.ca/ course_archive/ 2011-12/F / 4404, 2015
  8. Neocleous C. & Schizas C. Artificial Neural Network Learning: A Comparative Review. In: Vlahavas I.P., Spyropoulos C.D. (eds) Methods and Applications of  Artificial Intelligence,. Springer, Berlin, Heidelberg, doi: 10.1007/3-540-46014-4_27 pp.300-313,2012
  9. Taiwo, O. A. Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), ISBN: 978-953-307 -034-6, InTech, University of Portsmouth United Kingdom. Pp 3 –31, 2014
  10. Klassen, “A frame work for search forms classification” In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on , PP.1029-1034 Seoul, Korea, 14-17 Oct. 2012
  11. Anagnostopoulos, C. Anagnostopoulos, V. Loumos and E. Kayafas, “Classifying Web pages employing a probabilistic neural network”, Probabilistic Software, IEEE ,Vol.151, No.3,June 2014, PP.139-150
  12. M. Nayrolles and A. Hamou-Lhadj, "BUMPER: A Tool for Coping with Natural Language Searches of Millions of Bugs and Fixes," Software Analysis, Evolution, and Reengineering (SANER), IEEE pp. 649-652. 2016
  13. T. Cheng, K. Chakrabarti, S. Chaudhuri, V. Narasayya and M. Syamala, "Data services for E-tailers leveraging web search engine assets," Data Engineering (ICDE),IEEE, 2013, pp. 1153-1164
  14. Jebaraj Ratnakumar, “An Implementation of Web Personalization Using Web Mining Techniques”, Journal Of Theoretical And Applied Information Technology, 2014 JATIT
  15. Tsuyoshi, M and Saito, K., “Extracting User‟s Interest for Web Log Data”, IEEE 2016, pp. 343-346, ISBN: 0-7695-2747-7
  16. R.Rajeshkanna and Dr.A.Saradha “ Multipath Load Balanced Congestion Control Routing Techniques in Mobile Adhoc Network”, International Journal of Scientific Research and Development,Vol.2015 Issues 5, ISSN:2321-0613
  17. Lidong Wang  Data Mining, Machine Learning and Big Data Analytics International Transaction of Electrical and Computer Engineers System. 2017, 4(2), 55-61. DOI: 10.12691/iteces-4-2-2ublished online: July 24, 2017
  18. https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-02-28

Issue

Volume 7, Issue 1, January-February-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
T. Mohana Priya, Dr.M.Punithavalli, Dr. R. Rajesh Kanna, " Conceptual Review on Machine Learning Algorithms for Classification Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 1, pp.215-222, January-February-2021.