Mr Solomon Ebenuwa


Research outputs

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

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

Handling Imbalanced Classes: Feature Based Variance Ranking Techniques for Classification

Ebenuwa, S. 2019. Handling Imbalanced Classes: Feature Based Variance Ranking Techniques for Classification. PhD Thesis University of East London School of Architecture, Computing and Engineering https://doi.org/10.15123/uel.88183

Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data

Ebenuwa, S., Sharif, M., Alazab, Mamoun and Al-Nemrat, A. 2019. Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data. IEEE Access. 7, pp. 24649-24666. https://doi.org/10.1109/ACCESS.2019.2899578
  • 389
    total views of outputs
  • 919
    total downloads of outputs
  • 17
    views of outputs this month
  • 27
    downloads of outputs this month