Mi Young Lee

Artificial Intelligence · with Baik · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Mi Young Lee trusts overall

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

  1. Jorma Laurikkala
  2. Tom Fawcett
  3. Gustavo E. A. P. A. Batista
  4. Guillaume Lemaitre
  5. Flavio Luiz de Moraes Barboza
  6. Hui Han
  7. Nitesh V. Chawla
  8. Haibo He

Who trusts Mi Young Lee overall

  1. Dang Lien Minh

Papers from Mi Young Lee's perspective

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

  1. SMOTE: Synthetic Minority Over-sampling Technique
  2. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
  3. A study of the behavior of several methods for balancing machine learning training data
  4. A decision-theoretic generalization of on-line learning and an application to boosting
  5. ADASYN: Adaptive synthetic sampling approach for imbalanced learning (IEEE World Congress on Computational Intelligence)
  6. Improving Identification of Difficult Small Classes by Balancing Class Distribution (Conference on Artificial Intelligence in Medicine in Europe)
  7. An introduction to ROC analysis
  8. Scikit-learn: Machine Learning in Python
  9. An instance level analysis of data complexity (Machine-mediated learning)
  10. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

Their papers