An Empirical Methodology to Examine the Effect of Meta Classifiers in J48 and Random Tree in Weather Data

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.