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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| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2011
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| الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/31941/5/NurMaisarahMohdSobranMFKE2011.pdf |
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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 |