Dina Elreedy

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Dina Elreedy trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Nitesh V. Chawla
  2. Alberto Fernández
  3. Salvador Garcia
  4. Ricardo Barandela
  5. Nathalie Japkowicz
  6. Kevin W. Bowyer
  7. Francisco Herrera
  8. Gary M. Weiss
  9. José Salvador Sánchez
  10. L. Hall

Papers from Dina Elreedy's perspective

Ordered by how strongly the researchers Dina Elreedy trusts endorse each paper. Same corpus, a different order for every researcher.

  1. SMOTE: Synthetic Minority Over-sampling Technique
  2. Strategies for learning in class imbalance problems (Pattern Recognition)
  3. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  4. Fast Effective Rule Induction
  5. Robust Classification for Imprecise Environments
  6. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (Journal of Artificial Intelligence Research)
  7. A study of the behavior of several methods for balancing machine learning training data
  8. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
  9. MetaCost: a general method for making classifiers cost-sensitive
  10. SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Their papers