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Supervised Learning Techniques in Big Data: A New Perspective

S. Charles, D. Charles Raj

Abstract


Big data is an emerging technology, which enables to determine the facts from various repositories using analytics. In an earlier stage, the researchers are not able to handle huge amount of data. The various techniques are employed for finding the accuracy of huge amount of data. In this context, a machine learning technique helps to predict the information from big data repositories. In machine learning techniques, there are supervised, semi-supervised and unsupervised learning schemes are used to predict the information. In this paper, supervised techniques and their issues are considered for the discussion in various applications of big data.


Keywords


Big Data, Supervised Classification, Decision Tree, Support Vector Machine.

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References


XindongWu,Xingquan Zhu, Gong-Qing Wn, and Wei Ding, “Data Mining with Big Data”, Transaction on knowledge and data engineering, vol 26, No. 1.1041-4347/14 January 2014, IEEE.

T.M. Mitchell. Machine Learning. McGraw-Hill New York, 1997.

O. Chapelle, B. Chilkopf, A. Zien. Semi-Supervised Learning (Adaptive Computation and Machine Learning Series). MIT Press, Cambridge, 2006.

C.M. Bishop. Pattern Recognition and Machine Learning.Springer, New Yerk, 2006.

A. Mohri, A. Rostamizadeh and A. Talwallker. Foundations of Machine Learning. The MIT Press, Cambridge, 2012.

S.B. Kotsiantis, “Supervised Machine Learning: A Review of classification Techniques”, Informatica, Vol. 31, No. 3, pp. 249-268, 2007.

Richard S. Sutton and Andrew G. Barto,”Reinforcement Learning: An Introduction”, Cambridge, MA: MIT Press, 1998.

YongjunPiao, Hyun Woo Park, Cheng Hao Jin, Kenu HoRyu, “Ensemble Methods for Classification of High-Dimensional Data”, 978-4799-3919-0/14, 2014, IEEE.

Shan Suthaharan,”Big Data Classification: Problem and challenges in Network Intrusion Prediction with Machine Learning”, Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC 27402,USA,2012.

S.B. Kotsiantis, “Supervised machine learning: A review of classification techniques”, Informatica 31,249-268, 2007.

VitthaYenkar,prof. MahipBartere,”Review on Data M ining with Big Data”, internation Journal of computer science and Mobile Computing, vol.3 Issues.4 pg. 97-102,April-2014.

Hugo jair Escalante, “A Comparison of Outlier Detection Algorithm for Machine Learning”, CIC-2005 CongresoInternacional en Computacion-IPN, 2005.

Steven L. Salzberg, “Book Review: C45: Programs for Machine Learning by J Rose Quinlan.Inc., 1993”, Machine Learning, vol.16, No.3, pp235-240, 1994.

MehryarMohri, AfshinRostamizadeh and AmeetTalwalkar, “Foundations of Machine Learning “, one Rogers Street Cambridge MA:The MIT Press,2012.

Olivier Bouquet, St’ ephaneBoucheron and Gabor Lugosi, ”Introduction to Statistical Learning Theroy”, lecture notes in computer science, vol.3176,pp.175-213,2004.

Isadora Jacob Good, “probability and the weighing of Evidence”, The University of Wisconsin-Madison: Charles Griffin, 1950.

Jiecheng, Russell Greiner,JonathanKelly,David Bell and WeiruLiu,”Learning Bayesian networks from data: An information –Theory based approach”, The Artificial Intelligencee Journal, vol.137,pp.43-90,2002.


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