Prediction for Production Rate of Paddy Using Bagged Classifier Based on NBC

Abstract

A popular method for creating an accurate classifier from a set of training data is to train several classifiers, and then to combine their predictions. One way to generate an ensemble of simple Bayesian classifiers is Bagging which learns a set of independent models by bootstrapping the data to get a separate training set and then inducing a new Naive Bayesian Classifier (NBC) on this data set. This is then repeated a number of times. The models are then combined by using majority voting of the predicted classes. In this system, simple Bayesian classifier and Bagging ensemble of Bayesian classifiers are used to classify the class label of an unknown sample. The implemented system evaluates the production rate of paddy on the paddy training data set that are surveyed from the Department of Agriculture, Bago region (west), Pyay.