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MiNB : minority sensitive naïve Bayesian algorithm for multi-class classification of unbalanced data
Joint Authors
Barot, Pratikkumar
Jethva, Harikrishna
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 4 (31 Jul. 2022), pp.609-616, 8 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
The unbalanced nature of data makes it tough to achieve the desire performance goal for classification algorithms.
The sub-optimal prediction system isn't a viable solution due to the high misclassification cost of minority events.
Thus accurate imbalanced data classification could be a path changer for prediction in domains like medical diagnosis, judiciary, and disaster management systems.
To date, most of the existing studies of imbalanced data are for the binary class dataset and supported by data sampling techniques that suffer from loss of information and over-fitting.
In this paper, we present the modified naïve Bayesian algorithm for unbalanced data classification that eliminates the requirement of data level sampling.
We compared our proposed model with the data sampling technique and cost-sensitive techniques.
We use minority sensitive TP Rate, class-specific misclassification rate, and overall performance parameters such as accuracy, f-measure and G-mean.
The result shows that our proposed algorithm shows a more optimal result for unbalanced data classification.
Results shows reduction in misclassification rate and improve predictive performance for the minority class.
American Psychological Association (APA)
Barot, Pratikkumar& Jethva, Harikrishna. 2022. MiNB : minority sensitive naïve Bayesian algorithm for multi-class classification of unbalanced data. The International Arab Journal of Information Technology،Vol. 19, no. 4, pp.609-616.
https://search.emarefa.net/detail/BIM-1437332
Modern Language Association (MLA)
Barot, Pratikkumar& Jethva, Harikrishna. MiNB : minority sensitive naïve Bayesian algorithm for multi-class classification of unbalanced data. The International Arab Journal of Information Technology Vol. 19, no. 4 (Jul. 2022), pp.609-616.
https://search.emarefa.net/detail/BIM-1437332
American Medical Association (AMA)
Barot, Pratikkumar& Jethva, Harikrishna. MiNB : minority sensitive naïve Bayesian algorithm for multi-class classification of unbalanced data. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 4, pp.609-616.
https://search.emarefa.net/detail/BIM-1437332
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 614-616
Record ID
BIM-1437332