UCSY's Research Repository

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

Show simple item record

dc.contributor.author Htaik, Ei Yamin
dc.contributor.author Aye, Aye
dc.date.accessioned 2019-07-18T14:13:13Z
dc.date.available 2019-07-18T14:13:13Z
dc.date.issued 2017-12-27
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/946
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Eighth Local Conference on Parallel and Soft Computing en_US
dc.title Prediction for Production Rate of Paddy Using Bagged Classifier Based on NBC en_US
dc.type Article en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository


My Account