Antonio Bahamonde

1.2k total citations
48 papers, 793 citations indexed

About

Antonio Bahamonde is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Antonio Bahamonde has authored 48 papers receiving a total of 793 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 14 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Antonio Bahamonde's work include Genetic and phenotypic traits in livestock (6 papers), Machine Learning and Data Classification (6 papers) and Text and Document Classification Technologies (6 papers). Antonio Bahamonde is often cited by papers focused on Genetic and phenotypic traits in livestock (6 papers), Machine Learning and Data Classification (6 papers) and Text and Document Classification Technologies (6 papers). Antonio Bahamonde collaborates with scholars based in Spain, United States and Switzerland. Antonio Bahamonde's co-authors include Oscar Luaces, Jorge Díez, Juan José del Coz, José Ramón Quevedo, José Barranquero Tolosa, Jaime Alonso, Amparo Alonso‐Betanzos, F. Goyache, Bertha Guijarro‐Berdiñas and José Ranilla and has published in prestigious journals such as SHILAP Revista de lepidopterología, Trends in Food Science & Technology and Expert Systems with Applications.

In The Last Decade

Antonio Bahamonde

46 papers receiving 752 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Antonio Bahamonde Spain 17 380 165 130 79 74 48 793
Oscar Luaces Spain 15 322 0.8× 164 1.0× 150 1.2× 52 0.7× 69 0.9× 40 713
Juan José del Coz Spain 18 523 1.4× 128 0.8× 164 1.3× 58 0.7× 102 1.4× 42 953
José Ramón Quevedo Spain 13 304 0.8× 102 0.6× 85 0.7× 55 0.7× 72 1.0× 46 600
Yun Xu China 17 152 0.4× 233 1.4× 82 0.6× 47 0.6× 174 2.4× 42 824
Yuan Rao China 18 341 0.9× 110 0.7× 127 1.0× 35 0.4× 56 0.8× 90 1.3k
Muhammad Bilal Pakistan 16 296 0.8× 150 0.9× 91 0.7× 30 0.4× 17 0.2× 76 884
Wilhelmiina Hämäläinen Finland 11 149 0.4× 133 0.8× 41 0.3× 29 0.4× 28 0.4× 21 441
Nadeem Akhtar India 18 288 0.8× 114 0.7× 200 1.5× 12 0.2× 34 0.5× 122 1.1k
Ruji P. Medina Philippines 14 395 1.0× 217 1.3× 340 2.6× 11 0.1× 31 0.4× 205 1.1k
Weiqing Min China 23 334 0.9× 167 1.0× 570 4.4× 18 0.2× 188 2.5× 66 1.6k

Countries citing papers authored by Antonio Bahamonde

Since Specialization
Citations

This map shows the geographic impact of Antonio Bahamonde'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 Antonio Bahamonde with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antonio Bahamonde more than expected).

Fields of papers citing papers by Antonio Bahamonde

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Antonio Bahamonde. 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 Antonio Bahamonde. The network helps show where Antonio Bahamonde may publish in the future.

Co-authorship network of co-authors of Antonio Bahamonde

This figure shows the co-authorship network connecting the top 25 collaborators of Antonio Bahamonde. A scholar is included among the top collaborators of Antonio Bahamonde 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 Antonio Bahamonde. Antonio Bahamonde 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.
Díez, Jorge, et al.. (2023). Users' photos of items can reveal their tastes in a recommender system. Information Sciences. 642. 119227–119227. 4 indexed citations
2.
Díez, Jorge, et al.. (2023). All-in-one picture: visual summary of items in a recommender system. Neural Computing and Applications. 35(27). 20339–20349.
3.
Díez, Jorge, et al.. (2020). Towards explainable personalized recommendations by learning from users’ photos. Information Sciences. 520. 416–430. 22 indexed citations
4.
Martínez‐Rego, David, et al.. (2020). Fast Distributed k NN Graph Construction Using Auto-tuned Locality-sensitive Hashing. ACM Transactions on Intelligent Systems and Technology. 11(6). 1–18. 11 indexed citations
5.
Martínez‐Rego, David, et al.. (2019). Large scale anomaly detection in mixed numerical and categorical input spaces. Information Sciences. 487. 115–127. 18 indexed citations
6.
Luaces, Oscar, Jorge Díez, & Antonio Bahamonde. (2018). A peer assessment method to provide feedback, consistent grading and reduce students' burden in massive teaching settings. Computers & Education. 126. 283–295. 27 indexed citations
7.
Luaces, Oscar, et al.. (2014). Interval prediction for graded multi-label classification. Pattern Recognition Letters. 49. 171–176. 5 indexed citations
8.
Luaces, Oscar, Jorge Díez, José Barranquero Tolosa, Juan José del Coz, & Antonio Bahamonde. (2012). Binary relevance efficacy for multilabel classification. Progress in Artificial Intelligence. 1(4). 303–313. 152 indexed citations
9.
Quevedo, José Ramón, Antonio Bahamonde, Miguel Pérez‐Enciso, & Oscar Luaces. (2011). Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(1). 88–97. 6 indexed citations
10.
Luaces, Oscar, José Ramón Quevedo, Miguel Pérez‐Enciso, et al.. (2010). Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models. Journal of Computational Biology. 17(12). 1711–1723. 1 indexed citations
11.
Coz, Juan José del, Jorge Díez, & Antonio Bahamonde. (2009). Learning Nondeterministic Classifiers. Journal of Machine Learning Research. 10(79). 2273–2293. 42 indexed citations
12.
Luaces, Oscar, et al.. (2009). Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples. Artificial Intelligence in Medicine. 45(1). 63–76. 12 indexed citations
13.
Luaces, Oscar, José Ramón Quevedo, Francisco Taboada, Guillermo M. Albaiceta, & Antonio Bahamonde. (2007). Prediction of probability of survival in critically ill patients optimizing the area under the ROC curve. International Joint Conference on Artificial Intelligence. 956–961. 5 indexed citations
14.
Díez, Jorge, P. Albertı́, G. Ripoll, et al.. (2006). Using machine learning procedures to ascertain the influence of beef carcass profiles on carcass conformation scores. Meat Science. 73(1). 109–115. 25 indexed citations
15.
Coz, Juan José del, Gustavo F. Bayón, Jorge Díez, et al.. (2004). Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM. Neural Information Processing Systems. 17. 321–328. 13 indexed citations
16.
Luaces, Oscar & Antonio Bahamonde. (2003). Inflating examples to obtain rules. International Journal of Intelligent Systems. 18(11). 1113–1143. 10 indexed citations
17.
Díez, Jorge, Antonio Bahamonde, Jaime Alonso, et al.. (2003). Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses. Meat Science. 64(3). 249–258. 19 indexed citations
18.
Goyache, F., et al.. (2000). Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles. INTELIGENCIA ARTIFICIAL. 4(10). 2 indexed citations
19.
Bahamonde, Antonio. (1994). Inductive properties of lattices. International Journal of Computer Mathematics. 54(3-4). 127–142. 2 indexed citations
20.
Bahamonde, Antonio. (1985). Partially-additive monoids. French digital mathematics library (Numdam). 26(3). 221–244. 3 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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