Fernando Bacao

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #12,635 of 35,345 by trust here, #21,878 by citations
• Information Systems and Management — 35 citations into it
• Sociology and Political Science — 24 citations into it
• Organizational Behavior and Human Resource Management — 20 citations into it
• Education — 13 citations into it
• Electrical and Electronic Engineering — 12 citations into it; #11,520 of 16,539 by trust here, #14,379 by citations
• Information Systems — 8 citations into it
• Statistics and Probability — 8 citations into it

Whom Fernando Bacao trusts overall

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

1. Tao Zhou
2. Anol Bhattacherjee
3. Nitesh V. Chawla
4. Alfred P. Rovai
5. Carlos Tam
6. Foster J. Provost
7. Jaka Demsar
8. David Sculley
9. Chun-Hua Hsiao
10. Aries Susanto

Who trusts Fernando Bacao overall

1. Justin Engelmann
2. Adamu Ali-Gombe
3. Mingle Xu
4. Fei Wang
5. Stefan Lessmann
6. Eyad Elyan

Papers from Fernando Bacao's perspective

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

1. Statistical Comparisons of Classifiers over Multiple Data Sets
2. Understanding Information Systems Continuance: An Expectation-Confirmation Model (MIS Q.)
3. Editorial: special issue on learning from imbalanced data sets
4. SMOTE: Synthetic Minority Over-sampling Technique
5. Hellinger distance decision trees are robust and skew-insensitive
6. Greedy function approximation: A gradient boosting machine.
7. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
8. A Multiple Resampling Method for Learning from Imbalanced Data Sets (International Conference on Climate Informatics)
9. Some methods for classification and analysis of multivariate observations
10. Circos: an information aesthetic for comparative genomics. (Genome Research)

Their papers

• Oversampling for Imbalanced Learning Based on K-Means and SMOTE (2017) — 940 citations
• What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? (2020) — 435 citations
• Effective data generation for imbalanced learning using conditional generative adversarial networks (2018) — 576 citations
• An e-Learning Theoretical Framework (2016) — 307 citations