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A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System

Sandeep Kumar Verma1 , Turendar Sahu2 , Manjit Jaiswal3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-3 , Page no. 1094-1101, Mar-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i3.10941101

Online published on Mar 31, 2019

Copyright © Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal, “A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1094-1101, 2019.

MLA Style Citation: Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal "A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System." International Journal of Computer Sciences and Engineering 7.3 (2019): 1094-1101.

APA Style Citation: Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal, (2019). A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System. International Journal of Computer Sciences and Engineering, 7(3), 1094-1101.

BibTex Style Citation:
@article{Verma_2019,
author = {Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal},
title = {A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {1094-1101},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3972},
doi = {https://doi.org/10.26438/ijcse/v7i3.10941101}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.10941101}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3972
TI - A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System
T2 - International Journal of Computer Sciences and Engineering
AU - Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 1094-1101
IS - 3
VL - 7
SN - 2347-2693
ER -

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Abstract

Machine Learning, a subset of Artificial Intelligence is very popular and emerging field of science which basically focus on designing the approach enabling the computer or machines to learn from the input provided or past experience. There are lots of application of Machine Learning in day-to-day life such as face detection and recognition, decision making in business forecasting, etc. It is also becoming the business subject to various giant enterprises like Amazon, Google, FaceBook, etc. In this paper, we have focused our discussion to some popular Supervised Machine Learning algorithms that are SVM, logistic regression, Multinomial Naive Bayes, KNN apart from some other supervised Machine Learning algorithms like Linear Regression, Linear Discriminant Analysis, Decision Tree, Random Forest, Naïve Bayes, etc. and we determine the most efficient classification algorithm based on the data set which is multiclass dataset. This research paper gives some clarity to the selection of algorithm specific to some application. And we have shown the comparative results.

Key-Words / Index Term

Machine Learning (ML), K-nearest neighbours (KNN), Logistic Regression (Log), Multinomial Naïve Bayes (MulNB), Support Vector Machine (SVM), Classifier

References

[1] A. Simon, M. S. Deo, S. Venkatesan, D.R. Ramesh Babu, “An Overview of Machine Learning and its Applications”, International Journal of Electrical Sciences and Engineering, 2015.
[2] S. B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques”, Informatica 31 (2007), pp.249-268, 2007.
[3] S. Das, A. Dey, A. Pal, N. Roy, “Applications of Artificial Intelligence in Machine Learning: Review and Prospect”, International Journal of Computer Applications (0975 – 8887), Vol. 115, No. 9, 2015.
[4] A. Dey, “Machine Learning Algorithms: A Review”, International Journal of Computer Science and Information Technologies, Vol. 7 (3), pp.1174-1179, 2016.
[5] P. Rani, “A Review of various KNN Techniques”, International Journal for Research in Applied Science & Engineering Technology, Vol. 5, Issue 8, 2017.
[6] Li-Yu Hu, Min-Wei Huang, Shih-Wen Ke, and Chih-Fong Tsai “The distance function effect on k-nearest neighbor classification for medical datasets”, Springerplus, Vol. 5(1), 2016.
[7] Chich-Min Ma, Wei-Shui Yang and Bor-wen Cheng, ”How the parameter of k-nearest neighbours Algorithm impact on the Best Classification Accuracy: In case of Parkinson Dataset”, Journal of applied sciences, Vol. 14 (2), pp.171-176,2014.
[8] Murat KORKMAZ, Selami GÜNEY, Şule Yüksel YİĞÎTER, “The importance of logistic regression implementations in the turkish livestock sector and logistic regression implementations/fields”, J.Agric. Fac. HR.U., 2012.
[9] C-Y J. Peng, K. L. Lee, G. M. Ingersoll, “An introduction to logistic regression analysis and reporting”. The Journal of Educational Research, 96(1), pp.3-14, 2002.
[10] M. Szumilas, “Explaining Odds Ratios”, Journal of the Canadian Academy of Child and Adolescent Psychiatry,Vol. 19(3), 2010.
[11] A. M. Kibriya, E. Frank, B. Pfahringer, G. Holmes, “Multinomial Naive Bayes for Text Categorization Revisited”, G.I. Webb and Xinghuo Yu (Eds.): AI 2004, LNAI 3339, Springer-Verlag Berlin Heidelberg, pp. 488–499, 2004.
[12] D. Mallampati, “An Efficient Spam Filtering using Supervised Machine Learning Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue 2, pp.33-37, 2018.
[13] A. McCallum, K. Nigam, “A Comparison of Event Model for Naïve Bayes Text Classification”, AAAI Technical Report WS-98-05, 752, 1998.
[14] R. Mohana, S. Sumathi, “Document classification using Multinomial Naïve Bayesian classifier”, International Journal of Science, Engineering and Technology Research, Vol. 3, Issue 5, 2014.
[15] H. Doshi, M. Zalte, “Performance of Naïve Bayes Classifier – Multinomial Model on Different Categories of Documents”, International Journal of Computer Applications, ETCSIT, 2011.
[16] Y. Tian, Y. Shi, X. Liu, “Recent advances on support vector machines research”, Technological And Economic Development OF Economy, Vol. 18 (1), pp.5–33, 2012.
[17] V. Sindhwani, S. S. Keerthi, “Large scale semi-supervised linear SVMs”, Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 477-484, 2006.
[18] M. A. Hearst, S.T. Dumais, E. Osman, J. C. Platt, B. Schölkopf, “Support vector machines”, Intelligent Systems and their Applications, IEEE, 1998.
[19] G. Kaur, K. Kaur, “Sentiment Detection from Punjabi Text using Support Vector Machine”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue 6, pp.39-46, 2017.
[20] Y. E. M. Idris, Li Jun, “Single to multiple kernel learning with four popular SVM kernels (survey)”, International Journal of Research in Engineering and Technology, Vol. 5, Issue 3, 2016.
[21] R. A. FISHER, “The use of multiple measurements in taxonomic problems”, Annual Eugenics, Vol. 7, Part 2, pp.179-188, 1936.
[22] E. Anderson. "The species problem in Iris". Annals of the Missouri Botanical Garden”, Vol. 23 (3), pp.457–509, 1936.