Guang‐Hui Fu

471 total citations
26 papers, 366 citations indexed

About

Guang‐Hui Fu is a scholar working on Artificial Intelligence, Molecular Biology and Analytical Chemistry. According to data from OpenAlex, Guang‐Hui Fu has authored 26 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 6 papers in Molecular Biology and 6 papers in Analytical Chemistry. Recurrent topics in Guang‐Hui Fu's work include Imbalanced Data Classification Techniques (12 papers), Spectroscopy and Chemometric Analyses (6 papers) and Advanced Statistical Methods and Models (4 papers). Guang‐Hui Fu is often cited by papers focused on Imbalanced Data Classification Techniques (12 papers), Spectroscopy and Chemometric Analyses (6 papers) and Advanced Statistical Methods and Models (4 papers). Guang‐Hui Fu collaborates with scholars based in China and United Kingdom. Guang‐Hui Fu's co-authors include Lunzhao Yi, Jianxin Pan, Qing‐Song Xu, Dongsheng Cao, Yi‐Zeng Liang, Qian‐Nan Hu, Liangxiao Zhang, Hong‐Dong Li, Feng Xu and Jiabao Wang and has published in prestigious journals such as BMC Bioinformatics, Information Sciences and Chemometrics and Intelligent Laboratory Systems.

In The Last Decade

Guang‐Hui Fu

23 papers receiving 354 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Guang‐Hui Fu China 10 125 100 58 50 42 26 366
Tom Howley Ireland 5 142 1.1× 43 0.4× 41 0.7× 30 0.6× 25 0.6× 5 332
Marcos Gestal Spain 13 59 0.5× 53 0.5× 131 2.3× 91 1.8× 14 0.3× 40 402
Dezhao Chen China 10 128 1.0× 57 0.6× 26 0.4× 62 1.2× 136 3.2× 43 300
Wenjie You China 8 118 0.9× 30 0.3× 86 1.5× 20 0.4× 12 0.3× 18 300
Brieuc Conan‐Guez France 7 163 1.3× 33 0.3× 32 0.6× 20 0.4× 25 0.6× 13 272
Liyao Ma China 9 96 0.8× 22 0.2× 11 0.2× 32 0.6× 17 0.4× 31 362
Sanparith Marukatat Thailand 11 97 0.8× 23 0.2× 42 0.7× 9 0.2× 19 0.5× 56 541
Alireza Mehridehnavi Iran 13 74 0.6× 20 0.2× 134 2.3× 171 3.4× 15 0.4× 32 733
Mosa E. Hosney Egypt 9 276 2.2× 10 0.1× 44 0.8× 130 2.6× 47 1.1× 12 447
Gustavo Alonso Mexico 15 83 0.7× 27 0.3× 69 1.2× 11 0.2× 8 0.2× 37 592

Countries citing papers authored by Guang‐Hui Fu

Since Specialization
Citations

This map shows the geographic impact of Guang‐Hui Fu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Guang‐Hui Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guang‐Hui Fu more than expected).

Fields of papers citing papers by Guang‐Hui Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guang‐Hui Fu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Guang‐Hui Fu. The network helps show where Guang‐Hui Fu may publish in the future.

Co-authorship network of co-authors of Guang‐Hui Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Guang‐Hui Fu. A scholar is included among the top collaborators of Guang‐Hui Fu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guang‐Hui Fu. Guang‐Hui Fu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yi, Lunzhao, et al.. (2025). Stacking density estimation and its oversampling method for continuously imbalanced data in chemometrics. Chemometrics and Intelligent Laboratory Systems. 261. 105366–105366.
2.
Fu, Guang‐Hui, et al.. (2024). Sparse feature selection and rare value prediction in imbalanced regression. Information Sciences. 680. 121145–121145.
3.
4.
Wu, Hao, Dabing Ren, Ying Gu, et al.. (2024). Combinatory data‐independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities. Journal of Separation Science. 47(8). e2300848–e2300848. 1 indexed citations
5.
Huang, Niu, et al.. (2024). Affine combination‐based over‐sampling for imbalanced regression. Journal of Chemometrics. 38(3). 1 indexed citations
6.
Fu, Guang‐Hui, et al.. (2023). An adaptive loss backward feature elimination method for class-imbalanced and mixed-type data in medical diagnosis. Chemometrics and Intelligent Laboratory Systems. 236. 104809–104809. 3 indexed citations
7.
Fu, Guang‐Hui, et al.. (2023). ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression. Journal of Chemometrics. 37(11). 3 indexed citations
8.
Fu, Guang‐Hui, et al.. (2022). A Double-Penalized Estimator to Combat Separation and Multicollinearity in Logistic Regression. Mathematics. 10(20). 3824–3824. 4 indexed citations
9.
Zhang, Yunmei, Guang‐Hui Fu, Dabing Ren, et al.. (2021). Screening of lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography–high resolution mass spectrometry. Journal of Chromatography B. 1169. 122603–122603. 9 indexed citations
10.
Wang, Jiabao, et al.. (2021). AWSMOTE: An SVM-Based Adaptive Weighted SMOTE for Class-Imbalance Learning. Scientific Programming. 2021. 1–18. 16 indexed citations
11.
Fu, Guang‐Hui, et al.. (2020). Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data. BMC Bioinformatics. 21(1). 121–121. 27 indexed citations
12.
Fu, Guang‐Hui, Lunzhao Yi, & Jianxin Pan. (2019). LASSO‐based false‐positive selection for class‐imbalanced data in metabolomics. Journal of Chemometrics. 33(10). 12 indexed citations
13.
Fu, Guang‐Hui, et al.. (2019). Feature selection and classification by minimizing overlap degree for class-imbalanced data in metabolomics. Chemometrics and Intelligent Laboratory Systems. 196. 103906–103906. 27 indexed citations
14.
Fu, Guang‐Hui, et al.. (2016). Stable biomarker screening and classification by subsampling-based sparse regularization coupled with support vector machines in metabolomics. Chemometrics and Intelligent Laboratory Systems. 160. 22–31. 15 indexed citations
15.
Fu, Guang‐Hui, et al.. (2013). Group Variable Selection with Oracle Property by Weight-Fused Adaptive Elastic Net Model for Strongly Correlated Data. Communications in Statistics - Simulation and Computation. 43(10). 2468–2481. 6 indexed citations
16.
Fu, Guang‐Hui & Pan Wang. (2013). LASSO-Type Variable Selection Methods for High-Dimensional Data. Applied Mechanics and Materials. 444-445. 604–609. 1 indexed citations
17.
Fu, Guang‐Hui, Qing‐Song Xu, Hong‐Dong Li, Dongsheng Cao, & Yi‐Zeng Liang. (2011). Elastic Net Grouping Variable Selection Combined with Partial Least Squares Regression (EN-PLSR) for the Analysis of Strongly Multi-collinear Spectroscopic Data. Applied Spectroscopy. 65(4). 402–408. 43 indexed citations
18.
Fu, Guang‐Hui & Qing‐Song Xu. (2011). Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data. Communications in Statistics - Simulation and Computation. 41(2). 205–221. 4 indexed citations
19.
Cao, Dongsheng, Yi‐Zeng Liang, Qing‐Song Xu, et al.. (2011). Exploring nonlinear relationships in chemical data using kernel-based methods. Chemometrics and Intelligent Laboratory Systems. 107(1). 106–115. 76 indexed citations
20.
Fu, Guang‐Hui, Dongsheng Cao, Qing‐Song Xu, Hong‐Dong Li, & Yi‐Zeng Liang. (2011). Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors. Journal of Chemometrics. 25(2). 92–99. 17 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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