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  1. University of Computer Studies, Yangon
  2. Conferences

Scaling up the Naive Bayesian Classifier using Genetic and Decision Tree for feature selection

http://hdl.handle.net/20.500.12678/0000003545
http://hdl.handle.net/20.500.12678/0000003545
087cc140-35f0-4b81-8b44-0f2897f59f0c
7b19c37c-8c76-4dde-84ad-9ba8598f710a
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psc2010paper psc2010paper (84).pdf (37 Kb)
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Article
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Publication
Title
Title Scaling up the Naive Bayesian Classifier using Genetic and Decision Tree for feature selection
Language en
Publication date 2010-12-16
Authors
Thandar, Aye Mya
Description
This paper aims to scale up the NaïveBayesian Classifier using Genetic andDecision Tree for feature selection. The mainreason is to predict patient's breast cancerresult based on their diagnosis using thisscaled classifier. Naïve Bayes can suffer fromoversensitivity to redundant and/or irrelevantattributes. Several researchers haveemphasized on the issue of redundantattributes, as well as advantages of featureselection for the Naïve Bayesian Classifier. Inthis paper, Genetic algorithm is used to reduceredundant attributes in feature selection, andthen apply Decision tree algorithm to find anoptimal set of feature weights that improveclassification accuracy. By combining geneticalgorithm with decision tree, and this methodenhance the Bayesian classification toeliminate unnecessary features and producefast, accurate classifiers. Bayesian classifierrepresents each class with a probabilisticsummary, and finds the most likely class foreach example it is asked to classify.
Keywords
Bayesian Classifier, Genetic Algorithm, Decision tree, feature selection
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1285
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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