Classification of imbalanced datasets using naive bayes

Imbalanced data set had tendency to effect classifier performance in machine learning due to the greater influence given by majority data that overlooked the minority ones. But in classifying data, more important class is given by the minority data. In order to solve this problem, original Naïve Bay...

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Main Author: Mohd. Sobran, Nur Maisarah
Format: Thesis
Language:English
Published: 2011
Subjects:
Online Access:http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf
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id my-utm-ep.31941
record_format uketd_dc
spelling my-utm-ep.319412018-05-27T07:10:44Z Classification of imbalanced datasets using naive bayes 2011-05 Mohd. Sobran, Nur Maisarah TK Electrical engineering. Electronics Nuclear engineering Imbalanced data set had tendency to effect classifier performance in machine learning due to the greater influence given by majority data that overlooked the minority ones. But in classifying data, more important class is given by the minority data. In order to solve this problem, original Naïve Bayes was purposed as classifier for imbalanced data set. Our main interest is to investigate the performance of original Naïve Bayes classifier in imbalanced datasets. From the four UCI imbalanced datasets that been used, the purposed techniques show that, Naïve Bayes doing well in Herbaman’s datasets and satisfying results in other datasets. 2011-05 Thesis http://eprints.utm.my/id/eprint/31941/ http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic TK Electrical engineering
Electronics Nuclear engineering
spellingShingle TK Electrical engineering
Electronics Nuclear engineering
Mohd. Sobran, Nur Maisarah
Classification of imbalanced datasets using naive bayes
description Imbalanced data set had tendency to effect classifier performance in machine learning due to the greater influence given by majority data that overlooked the minority ones. But in classifying data, more important class is given by the minority data. In order to solve this problem, original Naïve Bayes was purposed as classifier for imbalanced data set. Our main interest is to investigate the performance of original Naïve Bayes classifier in imbalanced datasets. From the four UCI imbalanced datasets that been used, the purposed techniques show that, Naïve Bayes doing well in Herbaman’s datasets and satisfying results in other datasets.
format Thesis
qualification_level Master's degree
author Mohd. Sobran, Nur Maisarah
author_facet Mohd. Sobran, Nur Maisarah
author_sort Mohd. Sobran, Nur Maisarah
title Classification of imbalanced datasets using naive bayes
title_short Classification of imbalanced datasets using naive bayes
title_full Classification of imbalanced datasets using naive bayes
title_fullStr Classification of imbalanced datasets using naive bayes
title_full_unstemmed Classification of imbalanced datasets using naive bayes
title_sort classification of imbalanced datasets using naive bayes
granting_institution Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department Faculty of Electrical Engineering
publishDate 2011
url http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf
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