Cailing Dong

Artificial Intelligence · 2008

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Cailing Dong trusts overall

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

  1. Nathalie Japkowicz
  2. William W. Cohen
  3. Pedro M. Domingos
  4. Yoav Freund
  5. Foster J. Provost

Papers from Cailing Dong's perspective

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

  1. Robust Classification for Imprecise Environments
  2. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  3. A decision-theoretic generalization of on-line learning and an application to boosting
  4. Fast Effective Rule Induction
  5. MetaCost: a general method for making classifiers cost-sensitive
  6. Mining with rarity: a unifying framework
  7. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
  8. Learning from Imbalanced Data Sets: A Comparison of Various Strategies *
  9. Feature selection for text categorization on imbalanced data (SKDD)
  10. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

Their papers