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...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd. Sobran, Nur Maisarah
Format:	Thesis
Language:	English
Published:	2011
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

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
_version_	1747815882709532672

Classification of imbalanced datasets using naive bayes

Similar Items