Abstract:
Classification is a form of data analysis that can
be used to extract models describing important data
classes or to predict future data trends. Data
classification is a two step process. In this system, a
model is built on the training datasets by using the
Naive Bayesian classification algorithm. And then,
a model is used to test the unknown datasets. The
performance of classifier is estimated by using the
hold-out method. The Naive Bayesian (NB)
classifiers have been one of the most popular
techniques as basis of many classification
applications both theoretically and practically. This
system presents a Naive Bayesian classification
learning in order to evolve useful subset of paddy
features for classification task. This system is
determined the kind of paddy by using Naive
Bayesian classification.
