Hui Han

Artificial Intelligence · with Mao · 2005

A researcher in the Science corpus: 1 paper; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Hui Han trusts overall

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

  1. Nitesh V. Chawla
  2. Gary M. Weiss
  3. Steven L. Salzberg
  4. Gang Wu
  5. Larry Michael Manevitz
  6. Nathalie Japkowicz
  7. Mahesh V. Joshi
  8. Andrew Estabrooks
  9. Taeho C. Jo
  10. Andrew P. Bradley

Who trusts Hui Han overall

  1. Ajinkya More
  2. Bingyi Kang
  3. Jason Van Hulse
  4. Alberto Fernández
  5. Taghi M. Khoshgoftaar
  6. Victoria López
  7. Qi Dong
  8. Francisco Herrera
  9. Shuo Wang
  10. Rok Blagus

Papers from Hui Han's perspective

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

  1. SMOTE: Synthetic Minority Over-sampling Technique
  2. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  3. Evaluating boosting algorithms to classify rare classes: comparison and improvements (Proceedings 2001 IEEE International Conference on Data Mining)
  4. Programs for Machine Learning
  5. A study of the behavior of several methods for balancing machine learning training data
  6. One-Class SVMs for Document Classification (Journal of machine learning research)
  7. Mining with rarity: a unifying framework
  8. Class imbalances versus small disjuncts (SKDD)
  9. A decision-theoretic generalization of on-line learning and an application to boosting
  10. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

Their papers