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.
