UCSY's Research Repository

Implementation of Bagged Classifier Based on Naïve Bayesian Classification

Show simple item record

dc.contributor.author Oo, Myat Htun
dc.contributor.author Htun, Myat Thu Zar
dc.date.accessioned 2019-08-20T07:31:40Z
dc.date.available 2019-08-20T07:31:40Z
dc.date.issued 2009-08-03
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2137
dc.description.abstract Classification is an important data mining technique which predicts the class of a given data sample.Classification allocates new object to one out of a finite set of previously defined classes pm the basis of observations on several characteristics of the objects called attributes(or)features.The accuracy of the performance of classifier can be enhanced by using some techniques.One of these techniques is Bagging.The proposed system intends to implement a bagged classifier based on naïve Bayesian classifications to predict the class label of an unknown sample.The implemented classifier can be used as a supporting tool for decision making problems.The system will use german credit data approval at the bank as a case study.In this system,it will train based on credit data set and show how to get credit information with high accuracy for each customer. en_US
dc.language.iso en en_US
dc.publisher ThirdLocal Conference on Parallel and Soft Computing en_US
dc.title Implementation of Bagged Classifier Based on Naïve Bayesian Classification 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



Browse

My Account

Statistics