Humberto Bustince: h-index, Total Citations, and Citation Map
Humberto Bustince's h-index is 87 (319 i10-index, 31,003+ total citations across 2+ publications) according to Google Scholar as of May 2026. Humberto Bustince is affiliated with Professor of Computer Science, Universidad Publica de Navarra.
Humberto Bustince is a researcher affiliated with Professor of Computer Science, Universidad Publica de Navarra, specializing in Artificial Intelligence, Fuzzy Logic, Image Processing. Their work has been cited 31,003 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Humberto Bustince'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
- 87
- i10-Index
- 319
- Total Citations
- 31,003
- Citing Countries
- 10
As of May 2026.
Humberto Bustince has an h-index of 87 and 31,003 total citations across 2 publications, with research cited by institutions in 10 countries.
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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
20113,692
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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 the theoretical equivalence between vague sets and intuitionistic fuzzy sets, providing a foundational clarification in fuzzy logic theory.
The researcher provided a seminal synthesis of ensemble methods for class imbalance, establishing a foundational framework for bagging, boosting, and hybrid approaches in machine learning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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