An Empirical Methodology to Examine the Effect of Meta Classifiers in J48 and Random Tree in Weather Data
Divya R1, Anju Rajan K2, Deepa G3
1Divya R, P G Student, Department of Computer Science and IT Amrita School of Arts and Science, Amrita Vishwa Vidhyapeetham, Kochi, India.
2Anju Rajan K, P G Student, Department of Computer Science and IT Amrita School of Arts and Science, Amrita Vishwa Vidhyapeetham, Kochi, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 153-157 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3504049620/2020©BEIESP | DOI: 10.35940/ijitee.F3504.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Weather data interpretation has become vitally important in most domains of human activity and this is because in recent years, major changes have begun to impact climate globally – peninsular India is among the regions seriously affected with this and prediction has become a particularly urgent concern. In this work to bring out a better methodology to examine the weather data using Meta classifiers, a method is postulated by formulating it with Tree classifiers – J48 and Random Tree. Implementation phase has shown distinct results for both the classifiers. Regardless, we could conclude from this work that the effect of Meta Classifiers in J48 and Random Tree algorithm shows that efficiency can be improved by applying the same.
Keywords: AdaBoost, Bagging, Data Mining J48, Random Tree.
Scope of the Article: Mining and Warehousing