Shahrokh Asadi

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Shahrokh Asadi trusts overall

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

  1. Haibo He
  2. Jesús Alcalá-Fdez
  3. Rushi Longadge
  4. Gustavo E. A. P. A. Batista
  5. Alberto Fernández
  6. Hui Han
  7. Nitesh V. Chawla
  8. Mikel Galar
  9. Edwardo A. Garcia

Papers from Shahrokh Asadi's perspective

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

  1. SMOTE: Synthetic Minority Over-sampling Technique
  2. A study of the behavior of several methods for balancing machine learning training data
  3. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
  4. ADASYN: Adaptive synthetic sampling approach for imbalanced learning (IEEE World Congress on Computational Intelligence)
  5. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
  6. Improving Identification of Difficult Small Classes by Balancing Class Distribution (Conference on Artificial Intelligence in Medicine in Europe)
  7. KEEL: a software tool to assess evolutionary algorithms for data mining problems (Soft Computing - A Fusion of Foundations, Methodologies and Applications)
  8. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
  9. Classification of Imbalanced Data: a Review (International journal of pattern recognition and artificial intelligence)
  10. Learning from imbalanced data: open challenges and future directions

Their papers