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  1. University of Computer Studies, Yangon
  2. Conferences

An Implementation of Naive Bayesian based Bagging Method for Advertisements Prediction

http://hdl.handle.net/20.500.12678/0000004984
49fedfe1-8b2f-4644-9837-fbe01c6cc2a7
4917584e-25c2-49de-b058-4d09feca2be7
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psc2010paper psc2010paper (149).pdf (65 Kb)
Publication type Article
Upload type Publication
Title
An Implementation of Naive Bayesian based Bagging Method for Advertisements Prediction
en
Publication date 2010-12-16
Authors
Maung, Moh Cherry
Tun, Myat Thuzar
Description
Nowadays, the field of advertising is morespread. Broadcasting media receives advertisementsfrom advertisement companies. When programmeare shown, these advertisements used to bebroadcasted. Most of the advertisement companiesinstall to broadcast its advertisements whenadvertisement time. As Myanmar rule, it must bebroadcasted one third of the programme. So, mediacannot receive all advertisements. By using thissystem, it is easy to know which advertisementcompanies should be imperative if new programmebroadcast. In this system, Bagging and NaiveBayesian Classification methods are used. Baggingmethod is one of the well-known ensemble techniquesthat build bags of data of the same size of theoriginal data sets by applying random sampling withreplacement. Naive Bayesian Classification methodis widely used for probabilities estimations. Usingthe mixture of these algorithms, we get theprobabilities from multiple models .Then, averagingprobability values are calculated with baggingmethod. Finally, the system extracts the companynames with highest probabilities. Companies can beclassified into "classes". Another way, users can viewproducts information and reports at different sites.No need to look large amount of historical data.
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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