Lara Lusa

Artificial Intelligence · 2013–2017

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 11 citations into it; #18,304 of 35,345 by trust here, #30,347 by citations
• Molecular Biology — 9 citations into it
• Statistics and Probability — 9 citations into it; #6,688 of 12,932 by trust here, #8,740 by citations

Whom Lara Lusa trusts overall

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

1. Niko Speybroeck
2. Tom Fawcett
3. Trevor J. Hastie
4. Nitesh V. Chawla
5. Corinna Cortes
6. C. Drummond
7. Robert Tibshirani
8. Jason Van Hulse
9. Hui Han
10. David A. Cieslak

Who trusts Lara Lusa overall

1. Brian Kenji Iwana
2. Alberto Fernández
3. Seiichi Uchida

Papers from Lara Lusa's perspective

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

1. SMOTE: Synthetic Minority Over-sampling Technique
2. Support-Vector Networks
3. Random Forests
4. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
5. Experimental perspectives on learning from imbalanced data (International Conference on Machine Learning)
6. Classification and regression trees
7. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
8. Diagnosis of multiple cancer types by shrunken centroids of gene expression
9. Classification of gene microarrays by penalized logistic regression. (Biostatistics)
10. 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)

Their papers

• Firth's logistic regression with rare events: accurate effect estimates and predictions? (2017) — 391 citations
• SMOTE for high-dimensional class-imbalanced data (2013) — 1,106 citations