Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Article


Ebenuwa, S., Sharif, S., Al-Nemrat, A., Al-Bayatti, A. H., Alalwan, N., Alzahrani, A. I. and Alfarraj, O. 2019. Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All. Symmetry. 11 (Art. 1504). https://doi.org/10.3390/sym11121504
AuthorsEbenuwa, S., Sharif, S., Al-Nemrat, A., Al-Bayatti, A. H., Alalwan, N., Alzahrani, A. I. and Alfarraj, O.
Abstract

Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets.

JournalSymmetry
Journal citation11 (Art. 1504)
ISSN2073-8994
Year2019
PublisherMDPI
Publisher's version
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.3390/sym11121504
Web address (URL)https://doi.org/10.3390/sym11121504
Publication dates
Online11 Dec 2019
Publication process dates
Accepted25 Feb 2020
Deposited03 Mar 2020
FunderKing Saud University
Copyright holder© 2019 The Authors
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