Edurne Barrenechea: h-index, Total Citations, and Citation Map
Edurne Barrenechea's h-index is 41 (78 i10-index, 11,571+ total citations across 195+ publications) according to Google Scholar as of June 2026. Edurne Barrenechea is affiliated with Associate professor of Computer Science, Universidad Publica de Navarra.
Edurne Barrenechea is a researcher affiliated with Associate professor of Computer Science, Universidad Publica de Navarra, specializing in Artificial Intelligence, Fuzzy Logic, Image Processing. Their work has been cited 11,571 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Edurne Barrenechea's Citation Metrics
Bibliometric impact based on 195 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 41
- i10-Index
- 78
- Total Citations
- 11,571
- Citing Countries
- 56
As of June 2026.
Edurne Barrenechea has an h-index of 41 and 11,571 total citations across 195 publications, with research cited by institutions in 56 countries.
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We've mapped 5,000 of 11,571 citations for Edurne Barrenechea
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A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
20113,734
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
1 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for ensemble methods in class imbalance problems, subsequently advancing the field with evolutionary undersampling techniques to enhance performance on highly imbalanced datasets.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Edurne Barrenechea's research
Edurne Barrenechea is a researcher in Artificial Intelligence, Fuzzy Logic and Image Processing at Associate professor of Computer Science, Universidad Publica de Navarra. Their work has been cited 11,571 times across 195 publications (h-index 41), according to Google Scholar.
Their most-cited work, “A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches” (2011), has accumulated 3,734 citations. Other influential works include “An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes” (2011) with 1,002 citations and “A historical account of types of fuzzy sets and their relationships” (2015) with 564 citations.
Citations of Edurne Barrenechea's research come primarily from China, Spain and United States, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
