José-Ramón Cano

2.2k total citations · 1 hit paper
31 papers, 1.5k citations indexed

About

José-Ramón Cano is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, José-Ramón Cano has authored 31 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 7 papers in Computational Theory and Mathematics and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in José-Ramón Cano's work include Machine Learning and Data Classification (17 papers), Imbalanced Data Classification Techniques (12 papers) and Evolutionary Algorithms and Applications (8 papers). José-Ramón Cano is often cited by papers focused on Machine Learning and Data Classification (17 papers), Imbalanced Data Classification Techniques (12 papers) and Evolutionary Algorithms and Applications (8 papers). José-Ramón Cano collaborates with scholars based in Spain, Saudi Arabia and Mexico. José-Ramón Cano's co-authors include Francisco Herrera, Salvador García, Manuel Lozano, Joaquín Derrac, Julián Luengo, Naif Radi Aljohani, Óscar Cordón, Rabeeh Ayaz Abbasi, Ester Bernadó-Mansilla and Igor Zwir and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Expert Systems with Applications and Pattern Recognition.

In The Last Decade

José-Ramón Cano

31 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

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.

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study

2011 460 citations Salvador García, Joaquín Derrac et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
José-Ramón Cano Spain	15	1.3k	326	215	213	74	31	1.5k
Aaron Klein Germany	8	753 0.6×	236 0.7×	129 0.6×	84 0.4×	56 0.8×	25	1.2k
Pramod Kumar Singh India	18	573 0.5×	182 0.6×	138 0.6×	215 1.0×	35 0.5×	62	948
Jan N. van Rijn Netherlands	12	890 0.7×	203 0.6×	109 0.5×	81 0.4×	60 0.8×	33	1.2k
Hossein Ebrahimpour-Komleh Iran	15	586 0.5×	289 0.9×	181 0.8×	77 0.4×	33 0.4×	77	974
Anne M. P. Canuto Brazil	16	619 0.5×	254 0.8×	88 0.4×	192 0.9×	141 1.9×	135	935
Matthias Feurer Germany	6	651 0.5×	128 0.4×	123 0.6×	92 0.4×	57 0.8×	9	1.0k
Wei Weng China	15	543 0.4×	320 1.0×	178 0.8×	382 1.8×	36 0.5×	93	1.1k
Qiangfu Zhao Japan	15	634 0.5×	435 1.3×	181 0.8×	108 0.5×	153 2.1×	191	1.2k
Sheng-Uei Guan China	20	640 0.5×	326 1.0×	181 0.8×	123 0.6×	58 0.8×	117	1.2k

Countries citing papers authored by José-Ramón Cano

Since Specialization

Citations

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

Fields of papers citing papers by José-Ramón Cano

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José-Ramón Cano. 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 José-Ramón Cano. The network helps show where José-Ramón Cano may publish in the future.

Co-authorship network of co-authors of José-Ramón Cano

This figure shows the co-authorship network connecting the top 25 collaborators of José-Ramón Cano. A scholar is included among the top collaborators of José-Ramón Cano 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 José-Ramón Cano. José-Ramón Cano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Poorter, Eli De, et al.. (2025). Semi-supervised constrained clustering: an in-depth overview, ranked taxonomy and future research directions. Artificial Intelligence Review. 58(5). 1 indexed citations

Suárez, Juan Luis, et al.. (2023). Semi-supervised clustering with two types of background knowledge: Fusing pairwise constraints and monotonicity constraints. Information Fusion. 102. 102064–102064. 3 indexed citations

Rosales-Pérez, Alejandro, et al.. (2021). ME-MEOA/D C C : Multiobjective constrained clustering through decomposition-based memetic elitism. Swarm and Evolutionary Computation. 66. 100939–100939. 5 indexed citations

Rosales-Pérez, Alejandro, et al.. (2020). Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. 333–341. 3 indexed citations

Triguero, Isaac, et al.. (2020). Decomposition-Fusion for Label Distribution Learning. Information Fusion. 66. 64–75. 10 indexed citations

Cano, José-Ramón, Julián Luengo, & Salvador García. (2019). Label noise filtering techniques to improve monotonic classification. Neurocomputing. 353. 83–95. 8 indexed citations

Cano, José-Ramón, et al.. (2017). Prototype selection to improve monotonic nearest neighbor. Engineering Applications of Artificial Intelligence. 60. 128–135. 23 indexed citations

Cano, José-Ramón & Salvador García. (2017). Training set selection for monotonic ordinal classification. Data & Knowledge Engineering. 112. 94–105. 7 indexed citations

Jarwar, Muhammad Aslam, Rabeeh Ayaz Abbasi, Onaiza Maqbool, et al.. (2017). CommuniMents. International Journal on Semantic Web and Information Systems. 13(2). 87–108. 24 indexed citations

10.

García, Javier Gámez, et al.. (2016). Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms. International Journal of Computational Intelligence Systems. 9(1). 184–184. 12 indexed citations

11.

Cano, José-Ramón. (2013). Analysis of data complexity measures for classification. Expert Systems with Applications. 40(12). 4820–4831. 52 indexed citations

12.

García, Salvador, Joaquín Derrac, José-Ramón Cano, & Francisco Herrera. (2011). Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(3). 417–435. 460 indexed citations breakdown →

13.

Lozano, Manuel, Francisco Herrera, & José-Ramón Cano. (2008). Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Information Sciences. 178(23). 4421–4433. 127 indexed citations

14.

Cano, José-Ramón, Salvador García, & Francisco Herrera. (2008). Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes. Pattern Recognition Letters. 29(16). 2156–2164. 21 indexed citations

15.

García, Salvador, José-Ramón Cano, & Francisco Herrera. (2008). A memetic algorithm for evolutionary prototype selection: A scaling up approach. Pattern Recognition. 41(8). 2693–2709. 135 indexed citations

16.

Cano, José-Ramón, Francisco Herrera, Manuel Lozano, & Salvador García. (2007). Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection. Expert Systems with Applications. 35(4). 1949–1965. 14 indexed citations

17.

Cano, José-Ramón, Francisco Herrera, & Manuel Lozano. (2006). Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. Data & Knowledge Engineering. 60(1). 90–108. 76 indexed citations

18.

Cano, José-Ramón, Francisco Herrera, & Manuel Lozano. (2005). On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Applied Soft Computing. 6(3). 323–332. 59 indexed citations

19.

Alcalá, Rafael, José-Ramón Cano, Óscar Cordón, et al.. (2003). Linguistic modeling with hierarchical systems of weighted linguistic rules. International Journal of Approximate Reasoning. 32(2-3). 187–215. 24 indexed citations

20.

Cano, José-Ramón, Óscar Cordón, Francisco Herrera, & Luciano Sánchez. (2002). A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure. Journal of Intelligent & Fuzzy Systems. 12(3-4). 235–242. 14 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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