Tomasz Maciejewski

Artificial Intelligence · 2011

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

Whom Tomasz Maciejewski trusts overall

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

  1. Nitesh V. Chawla
  2. Jorma Laurikkala
  3. Jaka Demsar
  4. Haibo He
  5. Gustavo E. A. P. A. Batista
  6. Hui Han
  7. Chumphol Bunkhumpornpat
  8. Edwardo A. Garcia
  9. Ronaldo Cristiano Prati
  10. Wenyuan Wang

Who trusts Tomasz Maciejewski overall

  1. Qi Dong
  2. Chen Huang
  3. K. Ruwani
  4. José A. Sáez
  5. Shaogang Gong
  6. Yining Li
  7. M. I. M. Fernando
  8. Xiatian Zhu
  9. Julián Luengo
  10. Chen Change Loy

Papers from Tomasz Maciejewski's perspective

Ordered by how strongly the researchers Tomasz Maciejewski 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. Class imbalances versus small disjuncts (SKDD)
  5. Statistical Comparisons of Classifiers over Multiple Data Sets
  6. Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem (Pacific-Asia Conference on Knowledge Discovery and Data Mining)
  7. Improving Identification of Difficult Small Classes by Balancing Class Distribution (Conference on Artificial Intelligence in Medicine in Europe)
  8. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  9. MetaCost: a general method for making classifiers cost-sensitive
  10. Evaluating boosting algorithms to classify rare classes: comparison and improvements (Proceedings 2001 IEEE International Conference on Data Mining)

Their papers