Bibi Zabar

2005

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Whom Bibi Zabar trusts overall

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

  1. J. Ross Quinlan
  2. Chao Chen
  3. Nathalie Japkowicz
  4. Gary M. Weiss
  5. Philip Ka-Fai Chan

Who trusts Bibi Zabar overall

  1. Giovanna Menardi
  2. Siong Thye Goh

Papers from Bibi Zabar's perspective

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

  1. C4.5: Programs for Machine Learning
  2. SMOTE: Synthetic Minority Over-sampling Technique
  3. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
  4. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (Journal of Artificial Intelligence Research)
  5. The Foundations of Cost-Sensitive Learning
  6. The class imbalance problem: A systematic study
  7. Using Random Forest to Learn Imbalanced Data
  8. Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection
  9. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown
  10. Fast Effective Rule Induction

Their papers