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Decision Tree for the Weather Forecasting

by Rajesh Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 2
Year of Publication: 2013
Authors: Rajesh Kumar
10.5120/13220-0620

Rajesh Kumar . Decision Tree for the Weather Forecasting. International Journal of Computer Applications. 76, 2 ( August 2013), 31-34. DOI=10.5120/13220-0620

@article{ 10.5120/13220-0620,
author = { Rajesh Kumar },
title = { Decision Tree for the Weather Forecasting },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 2 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number2/13220-0620/ },
doi = { 10.5120/13220-0620 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:51.267928+05:30
%A Rajesh Kumar
%T Decision Tree for the Weather Forecasting
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 2
%P 31-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Predicting the classification of data in a suitable class is a challenging task. It depends on various factors to predict the dependent variables. Since decision tree evaluation can be quantified and it is simple to use, so a model using decision tree has been proposed by the author to predict the event like fog, rain and thunder by inputting average temperature, humidity and pressure. Which can be used by farmers or by peoples of all walk of life in taking the intelligent decisions. This model can be used in machine learning and further the proposed model has scope for improvement as more and more relevant attributes can be used in predicting the dependent variable. Decision tree(Decision stump) has been implemented in Weka to facilitate the forecasting of weather. .

References
  1. Rokach, Lior; Maimon, O. (2008). Data mining with decision trees: theory and applications. World Scientific Pub Co Inc. ISBN 978-9812771711.
  2. Quinlan, J. R. , (1986). Induction of Decision Trees. Machine Learning 1: 81-106, Kluwer Academic Publishers
  3. Varun Chandola et al,(2009) Anamoly detection survey , ACM Computing Surveys, Vol. 41(3), Article 15.
  4. Breiman, Leo; Friedman, J. H. , Olshen, R. A. , & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. ISBN 978-0-412-04841-8.
  5. Mahesh V. Joshi, Vipin Kumar, and Ramesh C. Agrawal, Evaluating boosting algorithms to classify rare classes, First IEEE International Conference on Data Mining.
  6. Friedman, J. H. (1999). Stochastic gradient boosting. Stanford University.
  7. Hastie, T. , Tibshirani, R. , Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction. New York: Springer Verlag.
  8. Rodriguez et al. (2006), Rotation forest : A new classifier ensemble method, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10).
  9. Pages downloaded from http://www. anderson. ucla. edu/faculty/jason. frand/teacher/technologies/palace/datamining. htm
  10. Pages downloaded from http://www-users. cs. umn. edu/~kumar/papers/papers. html
  11. Hyafil, Laurent; Rivest, RL (1976). "Constructing Optimal Binary Decision Trees is NP-complete". Information Processing Letters 5 (1): 15–17. doi:10. 1016/0020-0190(76)90095-8.
  12. Shyam Boriah, Varun Chandola and Vipin Kumar,(2008),Similarity measure of categorical datas, In Proceedings of SIAM Data Mining Conference, Atlanta, GA.
  13. Principles of Data Mining. ( 2007). doi:10. 1007/978-1-84628-766-4. ISBN 978-1-84628-765-7.
  14. Horváth, Tamás; Yamamoto, Akihiro, eds. (2003). Inductive Logic Programming. Lecture Notes in Computer Science 2835. doi:10. 1007/b13700. ISBN 978-3-540-20144-1. [15 Anurag Srivastava et al. Parallel formulation of decision trees and classification algorithms,Kluwer academic publishers.
  15. pages downloaded from http://en. wikipedia. org/wiki/Decision_tree_learning
  16. Varun Chandola, Shyam Boriah, and Vipin Kumar,(2009) In Proceedings of SIAM Data Mining Conference, Sparks.
  17. Breiman, L. (1996). Bagging Predictors. "Machine Learning, 24": pp. 123-140.
  18. Pages downloaded from http://www-users. cs. umn. edu/~kumar/dmbook/dmslides/chap4_basic_classification. pdf
  19. Anurag Srivastava,E hong Han,Vipin Kumar,Vineet singh Kluwer academic publishers, Boston,'parallel formulation of decision tree- classification algorithms.
  20. pages downloaded from http://citeseer. ist. psu. edu/oliver93decision. html
  21. Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0-471-22852-4. OCLC 50055336. [23 Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth,(1996) ,"To Knowledge Discovery in Databases" 6, American Association for Artificial Intelligence. AI Magazine Volume 17 Number 3.
  22. pages downloaded from http://www. cs. ccsu. edu/~markov/ccsu_courses/DataMining-1. html
  23. Jiawei Han and Michelin Kamber ,"(2006) Data Mining Concepts and Techniques",2nd edition, Morgan Kaufman.
Index Terms

Computer Science
Information Sciences

Keywords

Decision tree Data mining Classification Genetic algorithm

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