Amri Napolitano

Artificial Intelligence · 2007–2011

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #6,314 of 35,345 by trust here, #9,170 by citations
• Statistical Learning Theory — 12 citations into it
• Information Systems — 7 citations into it
• Electrical and Electronic Engineering — 5 citations into it; #1,010 of 16,539 by trust here, #4,846 by citations
• Optimization & Training — 5 citations into it

Whom Amri Napolitano trusts overall

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

1. Nitesh V. Chawla
2. Gary M. Weiss
3. Foster J. Provost
4. J. Ross Quinlan
5. Jason Van Hulse
6. Yoav Freund
7. Kai Ming Ting
8. Kevin W. Bowyer
9. Hui Han
10. William W. Cohen

Who trusts Amri Napolitano overall

1. Taghi M. Khoshgoftaar
2. George Forman
3. Chris Seiffert
4. David A. Cieslak
5. Joffrey L. Leevy
6. Jason Van Hulse
7. Zhongbin Sun
8. Michael Wasikowski
9. Shuo Wang
10. Rok Blagus

Papers from Amri Napolitano's perspective

Ordered by how strongly the researchers Amri Napolitano 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. Evaluating boosting algorithms to classify rare classes: comparison and improvements (Proceedings 2001 IEEE International Conference on Data Mining)
4. UCI Repository of machine learning databases
5. Robust Classification for Imprecise Environments
6. Experiments with a New Boosting Algorithm
7. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
8. The Foundations of Cost-Sensitive Learning
9. Random Forests
10. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (Journal of Artificial Intelligence Research)

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