Xiaoyan Yang

IEEE Access · with Alam · 2021

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

Whom Xiaoyan Yang trusts

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

  1. Charles Peter Elkan (in Optimization & Training)

Papers from Xiaoyan Yang's perspective

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

  1. A study of the behavior of several methods for balancing machine learning training data
  2. SMOTE: Synthetic Minority Over-sampling Technique
  3. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
  4. The Foundations of Cost-Sensitive Learning
  5. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
  6. ADASYN: Adaptive synthetic sampling approach for imbalanced learning (IEEE World Congress on Computational Intelligence)
  7. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  8. Improving Identification of Difficult Small Classes by Balancing Class Distribution (Conference on Artificial Intelligence in Medicine in Europe)
  9. Learning from imbalanced data: open challenges and future directions
  10. Learning From Imbalanced Data

Their papers