Aleksander Kotcz

Artificial Intelligence · 2004

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #6,945 of 35,345 by trust here, #8,439 by citations

Whom Aleksander Kotcz trusts overall

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

1. Bianca Zadrozny
2. Hongyu Guo
3. Nitesh V. Chawla

Who trusts Aleksander Kotcz overall

1. Andrea Dal Pozzolo
2. Nitesh V. Chawla
3. Charu C. Aggarwal
4. Yanmin Sun
5. Shuo Wang
6. Rukshan Batuwita
7. H. M.
8. Alberto Fernández

Papers from Aleksander Kotcz's perspective

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

1. SMOTE: Synthetic Minority Over-sampling Technique
2. MetaCost: a general method for making classifiers cost-sensitive
3. Robust Classification for Imprecise Environments
4. Learning and making decisions when costs and probabilities are both unknown (Knowledge Discovery and Data Mining)
5. The Foundations of Cost-Sensitive Learning
6. Evaluating boosting algorithms to classify rare classes: comparison and improvements (Proceedings 2001 IEEE International Conference on Data Mining)
7. Text Classification from Labeled and Unlabeled Documents using EM
8. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
9. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (Journal of Artificial Intelligence Research)
10. Estimating the Support of a High-Dimensional Distribution

Their papers

• Editorial: special issue on learning from imbalanced data sets (2004) — 2,064 citations