Talha Mahboob Alam

International Journal of Emerging Technologies in Learning (iJET) · 2021

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Talha Mahboob Alam trusts overall

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

  1. Charles Peter Elkan

Papers from Talha Mahboob Alam's perspective

Ordered by how strongly the researchers Talha Mahboob Alam 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. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  7. ADASYN: Adaptive synthetic sampling approach for imbalanced learning (IEEE World Congress on Computational Intelligence)
  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