Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique
CIT. Journal of Computing and Information Technology, Vol. 24, No. 2, June 2016, 181–194 181 doi: 10.20532/cit.2016.1002715 Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique Sudha Mohankumar1 and Valarmathi Balasubramanian2 1Information Technology Department, School of Information Technology and Engineering, VIT University, Vellore, India 2Software and Systems Engineering Department, School of Information Technology and Engineering, VIT University, Vellore, India Precise rainfall forecasting is a common challenge Information systems → Information retrieval → Re- across the globe in meteorological predictions. As trieval tasks and goals → Clustering and classification; rainfall forecasting involves rather complex dy- Applied computing → Operations research → Fore- namic parameters, an increasing demand for novel casting approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has at- Keywords: rainfall prediction, rough set, maximum tracted a wide variety of scientific applications and is frequency, optimal reduct, core features and accuracy extensively adopted in decision support systems. Al- though there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not ad- dressed in the present mechanisms. Therefore, this in- 1. Introduction vestigation has examined the feasibility of using rough set based feature selection and data mining methods, Rainfall forecast serves as an important di- namely Naïve Bayes (NB), Bayesian Logistic Re- saster prevention tool. Agricultural yields and gression (BLR), Multi-Layer Perceptron (MLP), J48, agriculture based industrial development more Classification and Regression Tree (CART), Random often than not, rely on natural water resources Forest (RF), and Support Vector Machine (SVM), to forecast rainfall.
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