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Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set

M. Vasantha1

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-4 , Page no. 504-506, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i4.504506

Online published on Apr 30, 2019

Copyright © M. Vasantha . 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: M. Vasantha, “Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.504-506, 2019.

MLA Style Citation: M. Vasantha "Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set." International Journal of Computer Sciences and Engineering 7.4 (2019): 504-506.

APA Style Citation: M. Vasantha, (2019). Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set. International Journal of Computer Sciences and Engineering, 7(4), 504-506.

BibTex Style Citation:
@article{Vasantha_2019,
author = {M. Vasantha},
title = {Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {7},
Issue = {4},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {504-506},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4065},
doi = {https://doi.org/10.26438/ijcse/v7i4.504506}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i4.504506}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4065
TI - Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set
T2 - International Journal of Computer Sciences and Engineering
AU - M. Vasantha
PY - 2019
DA - 2019/04/30
PB - IJCSE, Indore, INDIA
SP - 504-506
IS - 4
VL - 7
SN - 2347-2693
ER -

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Abstract

This paper discusses the different classification techniques. It also compares the efficiency of Tree Based Classifiers Random Forest, REP Tree and J48 Classifiers for the detection of masses in mammogram images and compares their robustness through various measures. The mammogram images used in this research have been taken from MIAS database and the classification is performed with the help of open source machine learning tool. Finding the best classifier is a tough task and this paper gives opportunity to researchers to drill down efficient research works for evaluating different classifiers

Key-Words / Index Term

Mammogram, Classification, Random Forest (RF), REP tree, J48 classifiers

References

[1 ] DM. Parkin Bray F, Ferlay J, Pisani P. Global cancer statistics, 202. CA Cancer J Clin, 55(2): PP 74- 108 , 2005
[2 ] American Cancer Society, “ Breast cancer facts & figures 2015-2016,”Atlanta, American Cancer Society , 2015
[3 ] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S.C. Cheran, F. De Carlo, G. De Nunzio, M.E. Fantacci, G. Forni, A. Lauria, E.L. Torres, R. Magro, G.L. Masala, P. Oliva, M.Quarta, G. Raso, A. Retico, S. Tangaro: "Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Network", IEEE Nuclear Science Symposium Conference Record, Rome, Italy, Vol. 5, pp. 2695-2699 2004.
[4 ] Kella BhanuJyothi, K. Hima Bindu and D. Suryanarayana, “ A Comparative Study of Random Forest & K-Nearest Neighbours on HAR dataset using Caret”, IJIRT, Volume 3, Issue 9 ISSN: 2349-6002., 2017.
[5 ] Dadye,Harold Buko and Richard Rimiru, “Effects of Different Pre-processing Strategies :A Comparative Study on Decision Tree Algorithms”, International journal of Digital Content Technology and its Applications 7.7 : pp 935-939,2013
[6 ] Liaw, Andy and Matthew Wiener, “Classification and regression by Random Forest”, R News : pp 18-22, 2002
[7 ] Goldstein, Benjamin .A, Polley Eric. C and Briggs, Farren. B.S, “Random Forests for Genetic Association Studies”, Statistical Applications in Genetics and Molecular Biology, Vol.10. Iss.1. Article 32, DOI: 10.2202/1544-6115.1691, 2011
[8 ] H. Hu , “Mining patterns in disease classification forests” Journal of Biomedical Informatics Volume 43 pp . 820-827, 2010
[9 ] I H Witten and E Frank . “Data mining: practical machine learning tools and techniques “– 2nd ed. , Morgan Kaufmann series in data management systems, United States of America, 2005
[10 ] Quinlan, J, ”Simplifying Decision trees”, International Journal of Man Machine Studies, 27(3), pp 221–234, 1987
[11 ] S.K. Jayanthi and S. Sasikala, “ REP Tree Classifier for identifying Link Spam in Web Search Engines” , IJSC, Volume 3, Issue 2 , pp 498 – 505, 2013
[12 ] WEKA: Waikato environment for knowledge analysis .http://www.cs.waikato.ac. nz/ml/weka
[13 ] Hussam Elbehiery,” Optical Fiber Cables Networks Defects Detection using Thermal Images Enhancement Techniques”, International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.1, pp.22-29 , 2018