Next Article in Journal
Model of Threats to Computer Network Software
Previous Article in Journal
Digital Watermarking Image Compression Method Based on Symmetric Encryption Algorithms
Open AccessArticle

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

1
School of Architecture, Computing and Engineering, UEL, Docklands Campus, 4-6 University Way, London E16 2RD, UK
2
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
3
Department of Computer Science, Community College, King Saud University, Riyadh, SA 11437, USA
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1504; https://doi.org/10.3390/sym11121504
Received: 5 October 2019 / Revised: 10 November 2019 / Accepted: 25 November 2019 / Published: 11 December 2019
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. View Full-Text
Keywords: variance ranking; imbalanced data; ranked order similarity; one-versus-one; one-versus-all variance ranking; imbalanced data; ranked order similarity; one-versus-one; one-versus-all
Show Figures

Figure 1

MDPI and ACS Style

Ebenuwa, S.H.; Sharif, M.S.; Al-Nemrat, A.; Al-Bayatti, A.H.; Alalwan, N.; Alzahrani, A.I.; Alfarraj, O. Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All. Symmetry 2019, 11, 1504.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop