Manuel S. Lazo-Cortés

482 total citations
28 papers, 220 citations indexed

About

Manuel S. Lazo-Cortés is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Manuel S. Lazo-Cortés has authored 28 papers receiving a total of 220 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 13 papers in Computational Theory and Mathematics and 8 papers in Information Systems. Recurrent topics in Manuel S. Lazo-Cortés's work include Rough Sets and Fuzzy Logic (12 papers), Machine Learning and Data Classification (8 papers) and Data Mining Algorithms and Applications (6 papers). Manuel S. Lazo-Cortés is often cited by papers focused on Rough Sets and Fuzzy Logic (12 papers), Machine Learning and Data Classification (8 papers) and Data Mining Algorithms and Applications (6 papers). Manuel S. Lazo-Cortés collaborates with scholars based in Mexico, Cuba and Ecuador. Manuel S. Lazo-Cortés's co-authors include José Ruíz-Shulcloper, José Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Ansel Y. Rodríguez‐González, Rosa María Valdovinos Rosas, J. Arturo Olvera-López, Raúl Monroy, Miguel Angel Medina‐Pérez, Claudia Feregrino-Uribe and René Cumplido and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

Manuel S. Lazo-Cortés

24 papers receiving 204 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manuel S. Lazo-Cortés Mexico 9 144 76 62 34 24 28 220
C. Shunmuga Velayutham India 8 155 1.1× 108 1.4× 47 0.8× 43 1.3× 15 0.6× 45 253
Aida de Haro-García Spain 13 354 2.5× 53 0.7× 57 0.9× 115 3.4× 13 0.5× 22 414
Bořivoj Melichar Czechia 6 119 0.8× 56 0.7× 42 0.7× 19 0.6× 7 0.3× 32 200
Yabo Xu China 6 120 0.8× 103 1.4× 148 2.4× 21 0.6× 46 1.9× 14 253
Karl Pfleger United States 5 117 0.8× 27 0.4× 58 0.9× 23 0.7× 12 0.5× 7 166
Anton Dries Belgium 9 186 1.3× 38 0.5× 61 1.0× 24 0.7× 56 2.3× 22 254
Marcin Szczuka Poland 9 98 0.7× 87 1.1× 63 1.0× 19 0.6× 35 1.5× 27 178
Bruno Crémilleux France 9 147 1.0× 84 1.1× 141 2.3× 29 0.9× 82 3.4× 25 258
Liam Cervante China 5 236 1.6× 117 1.5× 22 0.4× 48 1.4× 12 0.5× 5 298
David Ruano-Ordás Spain 13 248 1.7× 33 0.4× 220 3.5× 11 0.3× 28 1.2× 27 357

Countries citing papers authored by Manuel S. Lazo-Cortés

Since Specialization
Citations

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

Fields of papers citing papers by Manuel S. Lazo-Cortés

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Manuel S. Lazo-Cortés. 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 Manuel S. Lazo-Cortés. The network helps show where Manuel S. Lazo-Cortés may publish in the future.

Co-authorship network of co-authors of Manuel S. Lazo-Cortés

This figure shows the co-authorship network connecting the top 25 collaborators of Manuel S. Lazo-Cortés. A scholar is included among the top collaborators of Manuel S. Lazo-Cortés 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 Manuel S. Lazo-Cortés. Manuel S. Lazo-Cortés 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.
Lazo-Cortés, Manuel S., et al.. (2024). Shortest-length and coarsest-granularity constructs vs. reducts: An experimental evaluation. International Journal of Approximate Reasoning. 170. 109187–109187.
2.
Martínez-Trinidad, José Fco., et al.. (2023). An Algorithm for Computing All Rough Set Constructs for Dimensionality Reduction. Mathematics. 12(1). 90–90. 2 indexed citations
3.
Medina‐Pérez, Miguel Angel, et al.. (2021). Learning-Based Dissimilarity for Clustering Categorical Data. Applied Sciences. 11(8). 3509–3509. 5 indexed citations
4.
Lazo-Cortés, Manuel S., et al.. (2020). An Algorithm for Computing Minimum-Length Irreducible Testors. IEEE Access. 8. 56312–56320. 5 indexed citations
5.
Martínez-Trinidad, José Fco., et al.. (2020). MinReduct: A new algorithm for computing the shortest reducts. Pattern Recognition Letters. 138. 177–184. 9 indexed citations
6.
Martínez-Trinidad, José Fco., et al.. (2020). A PSO-based algorithm for mining association rules using a guided exploration strategy. Pattern Recognition Letters. 138. 8–15. 18 indexed citations
7.
Lazo-Cortés, Manuel S., et al.. (2019). On the Relation Between the Concepts of Irreducible Testor and Minimal Transversal. IEEE Access. 7. 82809–82816. 4 indexed citations
8.
Lazo-Cortés, Manuel S., et al.. (2017). A CUDA-based hill-climbing algorithm to find irreducible testors from a training matrix. Pattern Recognition Letters. 95. 22–28. 4 indexed citations
9.
Lazo-Cortés, Manuel S., et al.. (2017). Enhancing the Performance of YYC Algorithm Useful to Generate Irreducible Testors. International Journal of Pattern Recognition and Artificial Intelligence. 32(1). 1860001–1860001. 5 indexed citations
10.
Lazo-Cortés, Manuel S., et al.. (2016). A new algorithm for computing reducts based on the binary discernibility matrix. Intelligent Data Analysis. 20(2). 317–337. 6 indexed citations
11.
Lazo-Cortés, Manuel S., et al.. (2014). On the relation between rough set reducts and typical testors. Information Sciences. 294. 152–163. 21 indexed citations
12.
Lazo-Cortés, Manuel S., et al.. (2014). A parallel hill-climbing algorithm to generate a subset of irreducible testors. Applied Intelligence. 42(4). 622–641. 5 indexed citations
13.
Lazo-Cortés, Manuel S., et al.. (2013). Algorithm for shortest path search in Geographic Information Systems by using reduced graphs. SpringerPlus. 2(1). 291–291. 31 indexed citations
14.
Lazo-Cortés, Manuel S., et al.. (2012). Aplicación de un algoritmo de reducción de grafos al Método de los Grafos Dicromáticos//Applying a graph reduction algorithm to Dichromatic Graphs Method. SHILAP Revista de lepidopterología.
15.
Lazo-Cortés, Manuel S., et al.. (2012). Aplicación de un algoritmo de reducción de grafos al Método de los Grafos Dicromáticos. Redalyc (Universidad Autónoma del Estado de México). 15(2). 158–168.
16.
Lazo-Cortés, Manuel S., et al.. (2012). Graph-reduction algorithm for finding shortest path in Geographic Information Systems. IEEE Latin America Transactions. 10(6). 2201–2208. 3 indexed citations
17.
Lazo-Cortés, Manuel S., et al.. (2012). RBAC Extension Model for ERP Systems in Multidomain Environments. IEEE Latin America Transactions. 10(5). 2185–2190. 2 indexed citations
18.
Lazo-Cortés, Manuel S., et al.. (2012). A Fast Implementation for the Typical Testor Property Identification Based on an Accumulative Binary Tuple. International Journal of Computational Intelligence Systems. 5(6). 1025–1025. 9 indexed citations
19.
Lazo-Cortés, Manuel S., et al.. (2001). An overview of the evolution of the concept of testor. Pattern Recognition. 34(4). 753–762. 29 indexed citations
20.
Lazo-Cortés, Manuel S. & José Ruíz-Shulcloper. (1995). Determining the feature relevance for non-classically described objects and a new algorithm to compute typical fuzzy testors. Pattern Recognition Letters. 16(12). 1259–1265. 18 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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