Nicola Torelli

Artificial Intelligence · 2012–2014

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #10,335 of 35,345 by trust here, #24,417 by citations
• Information Systems — 10 citations into it
• Statistics and Probability — 9 citations into it; #10,485 of 12,932 by trust here, #12,603 by citations
• Accounting — 6 citations into it

Whom Nicola Torelli trusts overall

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

1. Leo Breiman
2. Nitesh V. Chawla
3. Bernard Walter Silverman
4. C. Drummond
5. David A. Cieslak
6. Jesse Davis
7. Gary King
8. Rukshan Batuwita
9. Gary M. Weiss
10. Kate McCarthy

Who trusts Nicola Torelli overall

1. Chakkrit Kla Tantithamthavorn
2. Paula Branco
3. Nicola Lunardon
4. Tarid Wongvorachan
5. Alberto Fernández
6. Ahmed E. Hassan

Papers from Nicola Torelli's perspective

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

1. SMOTE: Synthetic Minority Over-sampling Technique
2. The class imbalance problem: A systematic study
3. Bagging Predictors
4. Classification and regression trees
5. Learning Decision Trees for Unbalanced Data
6. Class imbalances versus small disjuncts (SKDD)
7. The relationship between Precision-Recall and ROC curves
8. Mining with rarity: a unifying framework
9. The effect of class distribution on classifier learning: an empirical study
10. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

Their papers

• Clustering of time series via non-parametric tail dependence estimation (2014) — 48 citations
• ROSE: a Package for Binary Imbalanced Learning (2014) — 471 citations
• Training and assessing classification rules with imbalanced data (2012) — 738 citations