職員瀏覽: Classification of imbalanced datasets using naive bayes

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...

全面介紹

Saved in:

書目詳細資料
主要作者:	Mohd. Sobran, Nur Maisarah
格式:	Thesis
語言:	English
出版:	2011
主題:	TK Electrical engineering. Electronics Nuclear engineering
在線閱讀:	http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf http://eprints.utm.my/id/eprint/31941/
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

id	my.utm.31941
record_format	eprints
spelling	my.utm.319412018-05-27T07:10:44Z http://eprints.utm.my/id/eprint/31941/ Classification of imbalanced datasets using naive bayes 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf Mohd. Sobran, Nur Maisarah (2011) Classification of imbalanced datasets using naive bayes. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
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
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
publishDate	2011
url	http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf http://eprints.utm.my/id/eprint/31941/
_version_	1643648894153785344
score	13.252575

Classification of imbalanced datasets using naive bayes

相似書籍