Jason Van Hulse

Artificial Intelligence · 2007–2011

A researcher in the Science corpus: 5 papers; trusts 24 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #5,543 of 35,345 by trust here, #8,439 by citations
• Statistical Learning Theory — 15 citations into it
• Information Systems — 9 citations into it
• Optimization & Training — 7 citations into it
• Algorithms & Complexity — 5 citations into it
• Electrical and Electronic Engineering — 5 citations into it; #1,011 of 16,539 by trust here, #4,846 by citations
• Evaluation & Benchmarks — 5 citations into it

Whom Jason Van Hulse trusts overall

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

1. Nitesh V. Chawla
2. J. Ross Quinlan
3. Gary M. Weiss
4. Foster J. Provost
5. Hui Han
6. Leo Breiman
7. Yoav Freund
8. Trevor J. Hastie
9. Kevin W. Bowyer
10. C. Drummond

Who trusts Jason Van Hulse overall

1. Taghi M. Khoshgoftaar
2. George Forman
3. Chris Seiffert
4. David A. Cieslak
5. Joffrey L. Leevy
6. Michael Wasikowski
7. Shuo Wang
8. Rok Blagus
9. Jonathan Berant
10. Nguyen Thai-Nghe

Papers from Jason Van Hulse's perspective

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

1. SMOTE: Synthetic Minority Over-sampling Technique
2. Fast Effective Rule Induction
3. UCI Repository of machine learning databases
4. Evaluating boosting algorithms to classify rare classes: comparison and improvements (Proceedings 2001 IEEE International Conference on Data Mining)
5. Robust Classification for Imprecise Environments
6. Experiments with a New Boosting Algorithm
7. Bagging Predictors
8. Random Forests
9. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
10. The Foundations of Cost-Sensitive Learning

Their papers

• Experimental perspectives on learning from imbalanced data (2007) — 842 citations
• RUSBoost: A Hybrid Approach to Alleviating Class Imbalance (2010) — 1,761 citations
• Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data (2011) — 321 citations
• RUSBoost: Improving classification performance when training data is skewed (2008) — 295 citations
• An Empirical Study of Learning from Imbalanced Data Using Random Forest (2007) — 352 citations