Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A review of feature selection methods on synthetic data
2012559 citationsVerónica Bolón‐Canedo, Noelia Sánchez‐Maroño et al.profile →
A review of feature selection methods in medical applications
Countries citing papers authored by Verónica Bolón‐Canedo
Since
Specialization
Citations
This map shows the geographic impact of Verónica Bolón‐Canedo'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 Verónica Bolón‐Canedo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Verónica Bolón‐Canedo more than expected).
Fields of papers citing papers by Verónica Bolón‐Canedo
This network shows the impact of papers produced by Verónica Bolón‐Canedo. 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 Verónica Bolón‐Canedo. The network helps show where Verónica Bolón‐Canedo may publish in the future.
Co-authorship network of co-authors of Verónica Bolón‐Canedo
This figure shows the co-authorship network connecting the top 25 collaborators of Verónica Bolón‐Canedo.
A scholar is included among the top collaborators of Verónica Bolón‐Canedo 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 Verónica Bolón‐Canedo. Verónica Bolón‐Canedo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Seijo-Pardo, Borja, Amparo Alonso‐Betanzos, Kristin P. Bennett, et al.. (2018). Analysis of imputation bias for feature selection with missing data.. The European Symposium on Artificial Neural Networks.1 indexed citations
9.
Bolón‐Canedo, Verónica, Beatriz Remeseiro, Konstantinos Sechidis, David Martínez‐Rego, & Amparo Alonso‐Betanzos. (2017). Algorithmic challenges in big data analytics.. The European Symposium on Artificial Neural Networks.4 indexed citations
Seijo-Pardo, Borja, Verónica Bolón‐Canedo, & Amparo Alonso‐Betanzos. (2016). Using a feature selection ensemble on DNA microarray datasets.. The European Symposium on Artificial Neural Networks.11 indexed citations
12.
Morán‐Fernández, Laura, Verónica Bolón‐Canedo, & Amparo Alonso‐Betanzos. (2016). Data complexity measures for analyzing the effect of SMOTE over microarrays.. The European Symposium on Artificial Neural Networks.5 indexed citations
13.
Remeseiro, Beatriz, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos, & Manuel G. Penedo. (2015). Learning features on tear film lipid layer cla ssification. The European Symposium on Artificial Neural Networks.2 indexed citations
14.
Bolón‐Canedo, Verónica, Michele Donini, & Fabio Aiolli. (2015). Feature and kernel learning.. Research Padua Archive (University of Padua).7 indexed citations
15.
Bolón‐Canedo, Verónica, et al.. (2015). On the use of machine learning techniques for the analysis of spontaneous reactions in automated hearing assessment. The European Symposium on Artificial Neural Networks.1 indexed citations
16.
Bolón‐Canedo, Verónica, et al.. (2014). Toward parallel feature selection from vertically partitioned data.. The European Symposium on Artificial Neural Networks.6 indexed citations
17.
Bolón‐Canedo, Verónica, Beatriz Remeseiro, Noelia Sánchez‐Maroño, & Amparo Alonso‐Betanzos. (2014). mC-ReliefF - An Extension of ReliefF for Cost-based Feature Selection. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 42–51.4 indexed citations
18.
Bolón‐Canedo, Verónica, Noelia Sánchez‐Maroño, & Amparo Alonso‐Betanzos. (2013). A distributed wrapper approach for feature selection.. The European Symposium on Artificial Neural Networks.1 indexed citations
19.
Bolón‐Canedo, Verónica, Sohan Seth, Noelia Sánchez‐Maroño, Amparo Alonso‐Betanzos, & José C. Prı́ncipe. (2011). Statistical dependence measure for feature selection in microarray datasets.. The European Symposium on Artificial Neural Networks.12 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
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Rankless may not fully capture the entirety of a scholar's output or impact.