Amelia Zafra

1.9k total citations
45 papers, 1.2k citations indexed

About

Amelia Zafra is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications. According to data from OpenAlex, Amelia Zafra has authored 45 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 12 papers in Computer Science Applications. Recurrent topics in Amelia Zafra's work include Evolutionary Algorithms and Applications (18 papers), Metaheuristic Optimization Algorithms Research (15 papers) and Image Retrieval and Classification Techniques (12 papers). Amelia Zafra is often cited by papers focused on Evolutionary Algorithms and Applications (18 papers), Metaheuristic Optimization Algorithms Research (15 papers) and Image Retrieval and Classification Techniques (12 papers). Amelia Zafra collaborates with scholars based in Spain, Netherlands and Mexico. Amelia Zafra's co-authors include Sebastián Ventura, Cristóbal Romero, Alberto Cano, Pedro G. Espejo, José Raúl Romero, Paul De Bra, César Hervás‐Martínez, José María Luna, Eva Gibaja and Mykola Pechenizkiy and has published in prestigious journals such as Expert Systems with Applications, Computers & Education and Information Sciences.

In The Last Decade

Amelia Zafra

41 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amelia Zafra Spain 19 668 464 353 186 111 45 1.2k
Juan A. Lara Spain 15 358 0.5× 401 0.9× 297 0.8× 51 0.3× 150 1.4× 61 1.0k
María A. Martínez Spain 10 315 0.5× 248 0.5× 184 0.5× 34 0.2× 71 0.6× 26 816
Juan F. Huete Spain 16 631 0.9× 78 0.2× 599 1.7× 186 1.0× 39 0.4× 79 1.3k
Abdulrahman Altalhi Saudi Arabia 18 344 0.5× 84 0.2× 152 0.4× 76 0.4× 30 0.3× 52 870
Tak-Lam Wong Hong Kong 21 598 0.9× 83 0.2× 344 1.0× 89 0.5× 48 0.4× 67 938
Kevin Waugh United Kingdom 15 700 1.0× 85 0.2× 158 0.4× 111 0.6× 25 0.2× 56 1.1k
Nazlia Omar Malaysia 19 982 1.5× 67 0.1× 392 1.1× 53 0.3× 154 1.4× 134 1.3k
Ming-Che Lee Taiwan 15 338 0.5× 192 0.4× 247 0.7× 64 0.3× 32 0.3× 53 709
Rajni Jindal India 17 312 0.5× 91 0.2× 403 1.1× 59 0.3× 34 0.3× 93 840
Lucas Drumond Germany 13 534 0.8× 212 0.5× 593 1.7× 175 0.9× 12 0.1× 32 914

Countries citing papers authored by Amelia Zafra

Since Specialization
Citations

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

Fields of papers citing papers by Amelia Zafra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amelia Zafra

This figure shows the co-authorship network connecting the top 25 collaborators of Amelia Zafra. A scholar is included among the top collaborators of Amelia Zafra 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 Amelia Zafra. Amelia Zafra 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.
Cano, Alberto, et al.. (2024). Hoeffding adaptive trees for multi-label classification on data streams. Knowledge-Based Systems. 304. 112561–112561. 2 indexed citations
2.
Zafra, Amelia, et al.. (2024). A survey of genetic algorithms for clustering: Taxonomy and empirical analysis. Swarm and Evolutionary Computation. 91. 101720–101720. 2 indexed citations
3.
Zafra, Amelia, et al.. (2023). An algorithm based on fuzzy ordinal classification to predict students’ academic performance. Applied Intelligence. 53(22). 27537–27559. 7 indexed citations
4.
Zafra, Amelia & Eva Gibaja. (2023). Nearest neighbor-based approaches for multi-instance multi-label classification. Expert Systems with Applications. 232. 120876–120876. 8 indexed citations
5.
Zafra, Amelia, et al.. (2022). Data mining in predictive maintenance systems: A taxonomy and systematic review. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 12(5). 37 indexed citations
6.
Zafra, Amelia, et al.. (2019). LEAC: An efficient library for clustering with evolutionary algorithms. Knowledge-Based Systems. 179. 117–119. 9 indexed citations
7.
Zafra, Amelia, et al.. (2019). Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowledge-Based Systems. 194. 105385–105385. 60 indexed citations
8.
Zafra, Amelia, et al.. (2018). A Hybrid Multi-Criteria Approach Using a Genetic Algorithm for Recommending Courses to University Students.. Educational Data Mining. 13 indexed citations
9.
Cano, Alberto, José María Luna, Amelia Zafra, & Sebastián Ventura. (2015). A classification module for genetic programming algorithms in JCLEC. Journal of Machine Learning Research. 16(1). 491–494. 16 indexed citations
10.
Cano, Alberto, Amelia Zafra, & Sebastián Ventura. (2013). Weighted Data Gravitation Classification for Standard and Imbalanced Data. IEEE Transactions on Cybernetics. 43(6). 1672–1687. 84 indexed citations
11.
Cano, Alberto, Amelia Zafra, & Sebastián Ventura. (2013). Parallel evaluation of Pittsburgh rule-based classifiers on GPUs. Neurocomputing. 126. 45–57. 13 indexed citations
12.
Zafra, Amelia & Sebastián Ventura. (2012). Multi-instance genetic programming for predicting student performance in web based educational environments. Applied Soft Computing. 12(8). 2693–2706. 30 indexed citations
13.
Zafra, Amelia, Eva Gibaja, María Luque, & Sebastián Ventura. (2011). An evaluation of the effectiveness of e-learning system as support for traditional classes. 431–435. 10 indexed citations
14.
Zafra, Amelia & Sebastián Ventura. (2010). G3P-MI: A genetic programming algorithm for multiple instance learning. Information Sciences. 180(23). 4496–4513. 24 indexed citations
15.
Zafra, Amelia & Sebastián Ventura. (2009). Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming.. Educational Data Mining. 2009(1). 309–318. 44 indexed citations
16.
Zafra, Amelia, Eva Gibaja, & Sebastián Ventura. (2009). Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining. Applied Soft Computing. 11(1). 93–102. 14 indexed citations
17.
Zafra, Amelia, Cristóbal Romero, Sebastián Ventura, & Enrique Herrera‐Viedma. (2009). Multi-instance genetic programming for web index recommendation. Expert Systems with Applications. 36(9). 11470–11479. 18 indexed citations
18.
Ventura, Sebastián, et al.. (2007). JCLEC: a Java framework for evolutionary computation. Soft Computing. 12(4). 381–392. 104 indexed citations
19.
20.
Zafra, Amelia, et al.. (1994). Evolución en el tratamiento y resultados en la hemorragia subaracnoidea en un servicio de neurocirugía. Neurocirugía. 5(2). 98–106. 1 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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