Yue-Shi Lee

Artificial Intelligence · 2009

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Yue-Shi Lee trusts overall

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

  1. Peter D. Turney
  2. Tian-Shyug Lee
  3. Nitesh V. Chawla
  4. Yoav Freund
  5. I-Fei Chen

Who trusts Yue-Shi Lee overall

  1. Hien M. Nguyen

Papers from Yue-Shi Lee's perspective

Ordered by how strongly the researchers Yue-Shi Lee trusts endorse each paper. Same corpus, a different order for every researcher.

  1. SMOTE: Synthetic Minority Over-sampling Technique
  2. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
  3. C4.5 and Imbalanced Data sets: Investigating the eect of sampling method, probabilistic estimate, and decision tree structure
  4. Types of Cost in Inductive Concept Learning (arXiv.org)
  5. Class imbalances versus small disjuncts (SKDD)
  6. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown
  7. The Foundations of Cost-Sensitive Learning
  8. SMOTEBoost: Improving Prediction of the Minority Class in Boosting
  9. Selective Sampling Using the Query by Committee Algorithm
  10. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines (Expert systems with applications)

Their papers