Mikel Galar: h-index, Total Citations, and Citation Map
Mikel Galar's h-index is 41 (78 i10-index, 12,684+ total citations across 2+ publications) according to Google Scholar as of May 2026. Mikel Galar is affiliated with Full Professor of Computer Science and Artificial Intelligence, Universidad Pública de Navarra.
Mikel Galar is a researcher affiliated with Full Professor of Computer Science and Artificial Intelligence, Universidad Pública de Navarra, specializing in Artificial Intelligence, Data Mining, Machine Learning. Their work has been cited 12,684 times. This profile visualizes their global influence, highlighting strong citation networks in Spain.
Mikel Galar's Citation Metrics
Bibliometric impact based on 2 indexed publications. Of these, 1 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 41
- i10-Index
- 78
- Total Citations
- 12,684
- Citing Countries
- 9
As of May 2026.
Mikel Galar has an h-index of 41 and 12,684 total citations across 2 publications, with research cited by institutions in 9 countries.
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We've mapped 5,000 of 12,684 citations for Mikel Galar
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Global Impact Map
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Top Cited Works
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
20123,692
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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.
The researcher established a foundational framework for addressing class imbalance through ensemble methods, significantly advancing machine learning theory and practice.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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