Carlos J. Mantas

1.2k total citations
35 papers, 798 citations indexed

About

Carlos J. Mantas is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Carlos J. Mantas has authored 35 papers receiving a total of 798 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 10 papers in Information Systems and 9 papers in Computational Theory and Mathematics. Recurrent topics in Carlos J. Mantas's work include Fuzzy Logic and Control Systems (13 papers), Neural Networks and Applications (13 papers) and Machine Learning and Data Classification (12 papers). Carlos J. Mantas is often cited by papers focused on Fuzzy Logic and Control Systems (13 papers), Neural Networks and Applications (13 papers) and Machine Learning and Data Classification (12 papers). Carlos J. Mantas collaborates with scholars based in Spain, Italy and Venezuela. Carlos J. Mantas's co-authors include Joaquín Abellán, Javier G. Castellano, Juan Luis Castro, Serafín Moral‐García, José M. Benítez, José M. Mantas, Alfonso Montella, Miguel Delgado‐Rodríguez, Fernando Rojas and Claudio Moraga and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and IEEE Transactions on Fuzzy Systems.

In The Last Decade

Carlos J. Mantas

33 papers receiving 760 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Carlos J. Mantas Spain 14 513 137 93 92 69 35 798
Javier G. Castellano Spain 14 467 0.9× 106 0.8× 144 1.5× 61 0.7× 110 1.6× 25 776
Jozef Zurada United States 19 320 0.6× 90 0.7× 76 0.8× 48 0.5× 116 1.7× 74 1.1k
Boris Kovalerchuk United States 13 343 0.7× 91 0.7× 23 0.2× 102 1.1× 124 1.8× 98 761
Ming S. Hung United States 16 286 0.6× 66 0.5× 69 0.7× 93 1.0× 163 2.4× 34 929
Dae-Ki Kang South Korea 14 569 1.1× 180 1.3× 249 2.7× 46 0.5× 88 1.3× 91 1.1k
Şeyda Ertekin United States 12 549 1.1× 78 0.6× 31 0.3× 24 0.3× 132 1.9× 32 912
Ligang Zhou Macao 15 546 1.1× 105 0.8× 493 5.3× 66 0.7× 149 2.2× 38 898
Sufang Zhang China 12 295 0.6× 83 0.6× 19 0.2× 44 0.5× 39 0.6× 49 833
Changzheng He China 13 283 0.6× 55 0.4× 55 0.6× 32 0.3× 78 1.1× 46 566
Rafael M. O. Cruz Canada 15 840 1.6× 143 1.0× 59 0.6× 31 0.3× 48 0.7× 52 1.2k

Countries citing papers authored by Carlos J. Mantas

Since Specialization
Citations

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

Fields of papers citing papers by Carlos J. Mantas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carlos J. Mantas. 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 Carlos J. Mantas. The network helps show where Carlos J. Mantas may publish in the future.

Co-authorship network of co-authors of Carlos J. Mantas

This figure shows the co-authorship network connecting the top 25 collaborators of Carlos J. Mantas. A scholar is included among the top collaborators of Carlos J. Mantas 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 Carlos J. Mantas. Carlos J. Mantas 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.
Moral‐García, Serafín, Javier G. Castellano, Carlos J. Mantas, & Joaquín Abellán. (2022). A new label ordering method in Classifier Chains based on imprecise probabilities. Neurocomputing. 487. 34–45. 3 indexed citations
2.
Moral‐García, Serafín, Carlos J. Mantas, Javier G. Castellano, & Joaquín Abellán. (2022). Using Credal C4.5 for Calibrated Label Ranking in Multi-Label Classification. International Journal of Approximate Reasoning. 147. 60–77. 12 indexed citations
3.
Castellano, Javier G., Serafín Moral‐García, Carlos J. Mantas, & Joaquín Abellán. (2020). On the Use of m-Probability-Estimation and Imprecise Probabilities in the Naïve Bayes Classifier. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 28(4). 661–682. 1 indexed citations
4.
Moral‐García, Serafín, Carlos J. Mantas, Javier G. Castellano, & Joaquín Abellán. (2019). Non-parametric predictive inference for solving multi-label classification. Applied Soft Computing. 88. 106011–106011. 8 indexed citations
5.
Mantas, Carlos J., Javier G. Castellano, Serafín Moral‐García, & Joaquín Abellán. (2018). A comparison of random forest based algorithms: random credal random forest versus oblique random forest. Soft Computing. 23(21). 10739–10754. 73 indexed citations
6.
Abellán, Joaquín, Carlos J. Mantas, & Javier G. Castellano. (2017). A Random Forest approach using imprecise probabilities. Knowledge-Based Systems. 134. 72–84. 44 indexed citations
7.
Mantas, Carlos J., Joaquín Abellán, & Javier G. Castellano. (2016). Analysis of Credal-C4.5 for classification in noisy domains. Expert Systems with Applications. 61. 314–326. 35 indexed citations
8.
Mantas, Carlos J. & Joaquín Abellán. (2014). Credal decision trees in noisy domains.. The European Symposium on Artificial Neural Networks. 5 indexed citations
9.
Abellán, Joaquín & Carlos J. Mantas. (2013). Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications. 41(8). 3825–3830. 110 indexed citations
10.
Mantas, Carlos J., et al.. (2008). Artificial Neural Networks are Zero-Order TSK Fuzzy Systems. IEEE Transactions on Fuzzy Systems. 16(3). 630–643. 23 indexed citations
11.
Mantas, Carlos J.. (2007). A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks. Soft Computing. 12(5). 493–514. 9 indexed citations
12.
Castro, Juan Luis, et al.. (2007). Extraction of fuzzy rules from support vector machines. Fuzzy Sets and Systems. 158(18). 2057–2077. 38 indexed citations
13.
Mantas, Carlos J., et al.. (2006). Extraction of similarity based fuzzy rules from artificial neural networks. International Journal of Approximate Reasoning. 43(2). 202–221. 33 indexed citations
14.
Mantas, Carlos J.. (2005). T-norms and t-conorms in multilayer perceptrons. European Society for Fuzzy Logic and Technology Conference. 1313–1318. 1 indexed citations
15.
Benítez, José M., Juan Luis Castro, Carlos J. Mantas, & Fernando Rojas. (2002). A neuro-fuzzy approach for feature selection. 2. 1003–1008. 10 indexed citations
16.
Castro, Juan Luis, Carlos J. Mantas, & José M. Benítez. (2002). Interpretation of artificial neural networks by means of fuzzy rules. IEEE Transactions on Neural Networks. 13(1). 101–116. 93 indexed citations
17.
Delgado‐Rodríguez, Miguel, Carlos J. Mantas, & M.C. Pegalajar. (2002). A fuzzy control based algorithm to train perceptrons. Proceedings of 6th International Fuzzy Systems Conference. 2. 1027–1031.
18.
Castro, Juan Luis, Miguel Delgado‐Rodríguez, & Carlos J. Mantas. (2001). A fuzzy rule-based algorithm to train perceptrons. Fuzzy Sets and Systems. 118(2). 359–367. 2 indexed citations
19.
Castro, Juan Luis, Miguel Delgado, & Carlos J. Mantas. (2000). SEPARATE: a machine learning method based on semi-global partitions. IEEE Transactions on Neural Networks. 11(3). 710–720. 7 indexed citations
20.
Castro, Juan Luis, et al.. (2000). MORSE: A general model to represent structured knowledge. International Journal of Intelligent Systems. 15(1). 27–43. 2 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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