A Study Review On Supervised Machine Learning Algorithms

International Journal of Computer Science and Engineering

© 2019 by SSRG - IJCSE Journal

Volume 6 Issue 8

Year of Publication : 2019

Authors : Shakti Chourasiya, Suvrat Jain

10.14445/23488387/IJCSE-V6I8P104

How to Cite?

Shakti Chourasiya, Suvrat Jain, "A Study Review On Supervised Machine Learning Algorithms," SSRG International Journal of Computer Science and Engineering , vol. 6,  no. 8, pp. 16-20, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I8P104

Abstract:

One of the center aims of machine learning is to teach system to utilize information or past experience to take care of a given issue. A decent number of fruitful utilizations of machine learning exist as of now, including classifier to be prepared on email messages to learn so as to recognize spam and non-spam messages, frameworks that break down past deals information to foresee client purchasing conduct, misrepresentation recognition and so on. Machine learning can be connected as affiliation examination through supervised learning, unsupervised learning and Reinforcement learning yet in this investigation we will concentrate on quality and shortcoming of supervised learning characterization calculations. The objective of supervised learning is to assemble a succinct model of the circulation of class names as far as indicator highlights. The subsequent classifier is then used to dole out class names to the testing examples where the estimations of the indicator highlights are known, however the estimation of the class mark is obscure. We are hopeful that this investigation will assist new scientists with guiding new research regions and to analyze the adequacy and impuissance of supervised learning calculations.

Keywords:

Supervised Machine Learning, SVM, DT, Classifier, Decision Trees

References:

[1] S. B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques”, Informatica, Vol. 31, No. 3, pp. 249-268, 2007.
[2] James Cussens, “Machine Learning”, IEEE Journal of Computing and Control, Vol. 7, No. 4, pp 164-168, 1996.
[3] Richard S. Sutton and Andrew G. Barto, “Reinforcement Learning: An Introduction”, Cambridge, MA: MIT Press, 1998.
[4] Victoria J. Hodge and Jim Austin, “A Survey of Outlier Detection Methodologies”, Artificial Intelligence Review, Vol. 22, No. 2, pp. 85-126, 2004.
[5] Karanjit Singh and Shuchita Upadhyaya, “Outlier Detection: Applications and Techniques”, International Journal of Computer Science Issues, Vol. 9, Issue. 1, No. 3, pp. 307-323, 2012.
[6] Hugo Jair Escalante, “A Comparison of Outlier Detection Algorithms for Machine Learning”, CIC-2005 Congreso Internacional en Computacion-IPN, 2005.
[7] Tom M. Mitchell, “Machine Learning: A Guide to Current Research”, The Springer International Series in Engineering and Computer Science Series, McGraw Hill, 1997.
[8] D. Aha, “Lazy Learning”, Dordrecht: Kluwer Academic Publishers, 1997.
[9] Ramon Lopez De Mantaras and Eva Armengol, “Machine learning from examples: Inductive and Lazy methods”, Data and Knowledge Engineering, Vol. 25, No. 1-2, pp. 99-123, 1998.
[10] Hamid Parvin, Hoseinali Alizadeh and Behrouz Minati, “A Modification on K-Nearest Neighbor Classifier”, Global
Journal of Computer Science and Technology, Vol. 10, No. 14 (Ver.1.0), pp. 37-41, 2010.
[11] Pierre Geurts, Alexandre Irrthum, Louis Wehenkel, “Supervised learning with decision tree-based methods in computational and systems biology”, Molecular BioSystems, Vol. 5, No. 12, pp. 1593-1605, 2009.
[12] L. Breiman, J. Friedman, R. A. Olsen and C. J. Stone, “Classification and Regression Trees”, Belmont, California: Wadsworth International Group, 1984.
[13] J. Quinlan, “C4.5: Programs for machine learning”, San Francisco, CA: Morgan Kaufmann, 1986.
[14] Masud Karim and Rashedur M. Rahman, “Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing”, Journal of Software Engineering and Applications, Vol. 6, No. 4, pp. 196-206, 2013.
[15] Earl B. Hunt, Janet Marin and Philip J. Stone, “Experiments in Induction”, New York: Academic Press, 1966.
[16] Leo Breiman, Jerome Friedman, Charles J. Stone and R. A. Olshen, “Classification and Regression Trees (Wadsworth Statistics/Probability)”, Chapman and Hall/CRC, 1984.
[17] Yen-Liang Chen and Lucas Tzu-Hsuan Hung, “Using decision trees to summarize associative classification rules”, Expert Systems with Applications, Vol. 36, No. 2, Part 1, pp. 2338-2351, 2009.
[18] Steven L. Salzberg, “Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Inc., 1993”, Machine Learning, Vol. 16, No. 3, pp. 235-240, 1994.
[19] Johannes Fürnkranz, “Separate-and-Conquer Rule Learning”, Artificial Intelligence Review, Vol. 13, pp. 3-54, 1999.
[20] Aijun An and Nick Cercone, “Discretization of continuous attributes for learning classification rules”, Third Pacific- Asia Conference on Methodologies for Knowledge Discovery & Data Mining, Vol. 1574, pp. 509-514, 1999.
[21] Olivier Bousquet, St´ephane Boucheron and G´abor Lugosi, “Introduction to Statistical Learning Theory”, Lecture Notes in Computer Science, Vol. 3176, pp. 175- 213, 2004.
[22] Olivier Pourret, Patrick Naim and Bruce Marcot, “Bayesian Networks: A Practical Guide to Applications”, Wiley Publishers, 2008.
[23] Isidore Jacob Good, “Probability and the Weighing of Evidence”, The University of Wisconsin - Madison: Charles Griffin, 1950.
[24] Shiliang Sun, Changshui Zhang and Guoqiang Yu, “A Bayesian Network Approach to Traffic Flow Forecasting”, IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No. 1, pp. 124-132, 2006.
[25] Jie Cheng, Russell Greiner, Jonathan Kelly, David Bell and Weiru Liu, “Learning Bayesian networks from data: An information-Theory based approach”, The Artificial Intelligence Journal, Vol. 137, pp. 43-90, 2002.
[26] Samy Bengio, Li Deng, Hugo Larochelle, Honglak Lee, and Ruslan Salakhutdinov, “Guest Editors’ Introduction: Special Section on Learning Deep Architectures”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 8, pp. 1795-1797, 2013.
[27] Qinghua Zheng, Zhaohui Wu, Xiaocheng Cheng, Lu Jiang and Jun Liu, “Learning to crawl deep web”, Information Systems, Vol. 38, No. 6, pp. 801-819, 2013.
[28] Xue-Wen Chen and Xiaotong Lin,” Big Data Deep Learning: Challenges and Perspectives”, IEEE Access Practical Innovations: Open Solutions and Access and IEEE, Vol. 2, pp. 514-525, 2014.