Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data
M. Manikandan1 , R. Mala2
Section:Research Paper, Product Type: Journal Paper
Volume-6 ,
Issue-10 , Page no. 895-899, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i10.895899
Online published on Oct 31, 2018
Copyright © M. Manikandan, R. Mala . 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.
View this paper at Google Scholar | DPI Digital Library
How to Cite this Paper
- IEEE Citation
- MLA Citation
- APA Citation
- BibTex Citation
- RIS Citation
IEEE Style Citation: M. Manikandan, R. Mala, “Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.895-899, 2018.
MLA Style Citation: M. Manikandan, R. Mala "Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data." International Journal of Computer Sciences and Engineering 6.10 (2018): 895-899.
APA Style Citation: M. Manikandan, R. Mala, (2018). Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data. International Journal of Computer Sciences and Engineering, 6(10), 895-899.
BibTex Style Citation:
@article{Manikandan_2018,
author = {M. Manikandan, R. Mala},
title = {Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2018},
volume = {6},
Issue = {10},
month = {10},
year = {2018},
issn = {2347-2693},
pages = {895-899},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3114},
doi = {https://doi.org/10.26438/ijcse/v6i10.895899}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i10.895899}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3114
TI - Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data
T2 - International Journal of Computer Sciences and Engineering
AU - M. Manikandan, R. Mala
PY - 2018
DA - 2018/10/31
PB - IJCSE, Indore, INDIA
SP - 895-899
IS - 10
VL - 6
SN - 2347-2693
ER -
VIEWS | XML | |
482 | 342 downloads | 315 downloads |
Abstract
Meteorological data analysis in the form of data mining is concerned to predict the knowledge of weather condition. To make an accurate prediction is one of the challenging of meteorologist to survey the weather condition efficiently. Decision tree algorithms are suitable for analyzing the data of meteorological behavior. By evaluates three algorithm of decision tree such as Random Forest, C4.5, C4.5 with Bootstrap aggregation, to analyse the time efficiency and accuracy of classification. These accuracy of algorithm when it operates on trained weather data of selected location. Those locations are selected through monsoon condition based on India country.
Key-Words / Index Term
Randon Forest, C4.5, C4.5 with Bootstrap Algorithm, Meterological Data, Accuracy,Time efficiency
References
[1] Hall, Mark, et al. "The WEKA data mining software: an update." ACM SIGKDD Explorations Newsletter 11.1 (2009): 10-18.
[2] T.F. Gonzales. “Clustering to minimize the maximum inter cluster distance”. Theoretical Computer Science,1985,38(2-3):293-306.
[3] Kannan, M., S. Prabhakaran, and P. Ramachandran. "Rainfall forecasting using data mining technique."(2010)
[4] Arun K Pujari, “Data mining techniques”, University Press (India). 2003.
[5] Jiawei Han Micheline Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publisher an imprint of Elsevier, 2006.
[6] L. Breiman, J. Friedman, R. Olshen and C. Stone.
“Classification and Regression Trees”, Wadsworth
International Group, Belmont, CA, 1984.
[7] Quinlan, J.R.. “C5.0 Online Tutorial”, (2003)
[8] Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q.,
Motoda, Z., Steinbach,M., Hand, D. J and Steinberg, D
(2008). “Top 10 Algorithms in Data Mining”,
Knowledge and Information Systems, 14 (1): 1-37.
[9] Schapire, R. “The strength of weak learnability”,
Machine Learning,(1990) 5(2): 197-227.
[10] Breiman, L . "Random Forests". Machine Learning
45 (1): 5–32. (2010)
[11] Freund, Y. Schapire, R. “Experiments with a new
boosting algorithm”, In Proceedings of the
Thirteenth International Conference on Machine
Learning, 148-156 Bari, Italy. (1996)
[12] Dietterich, T. G.. “An experimental comparison of
three methods for constructing ensembles of
decision trees: bagging, boosting and randomization”.
Machine learning, 40: 139-157. (2000).
[13] Opitz, D and Maclin, R "Popular Ensembl
Methods: An Empirical Study", 11: 169-198. (1999)
[14] Quinlan, J. R. “Bagging, Boosting and C4.5”,
AAAI/IAAI, 1: 725-730. (1996)
[15] M. Mayilvaganan, D. Kalpanadevi, “Comparison of
Classification Techniques for predicting the
performance of Students Academic Environment” in
(2014)