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Carlos Javier Mantas
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2020 – today
- 2022
- [j33]Serafín Moral-García, Carlos Javier Mantas, Javier G. Castellano
, Joaquín Abellán:
Using Credal C4.5 for Calibrated Label Ranking in Multi-Label Classification. Int. J. Approx. Reason. 147: 60-77 (2022) - [j32]Serafín Moral-García, Javier G. Castellano
A new label ordering method in Classifier Chains based on imprecise probabilities. Neurocomputing 487: 34-45 (2022) - [j31]Serafín Moral-García, Javier G. Castellano
Using extreme prior probabilities on the Naive Credal Classifier. Knowl. Based Syst. 237: 107707 (2022) - 2021
- [j30]Javier G. Castellano
A Decision Support Tool for Credit Domains: Bayesian Network with a Variable Selector Based on Imprecise Probabilities. Int. J. Fuzzy Syst. 23(7): 2004-2020 (2021) - [c10]Serafín Moral-García, Francisco Javier García Castellano, Carlos Javier Mantas, Joaquín Abellán:
Using Credal C4.5 for Calibrated Label Ranking in Multi-Label Classification. ISIPTA 2021: 220-228 - 2020
- [j29]Serafín Moral-García, Carlos Javier Mantas, Javier G. Castellano
Non-parametric predictive inference for solving multi-label classification. Appl. Soft Comput. 88: 106011 (2020) - [j28]Serafín Moral-García, Carlos Javier Mantas, Javier G. Castellano
Bagging of credal decision trees for imprecise classification. Expert Syst. Appl. 141 (2020) - [j27]Javier G. Castellano
On the Use of m-Probability-Estimation and Imprecise Probabilities in the Naïve Bayes Classifier. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 28(4): 661-682 (2020) - [c9]Serafín Moral, Carlos Javier Mantas, Javier G. Castellano, Joaquín Abellán:
Imprecise Classification with Non-parametric Predictive Inference. IPMU (2) 2020: 53-66
2010 – 2019
- 2019
- [j26]Serafín Moral-García
Decision Tree Ensemble Method for Analyzing Traffic Accidents of Novice Drivers in Urban Areas. Entropy 21(4): 360 (2019) - [j25]Carlos Javier Mantas:
Interpretation of first-order recurrent neural networks by means of fuzzy rules. J. Intell. Fuzzy Syst. 37(3): 4055-4070 (2019) - [j24]Serafín Moral-García, Carlos Javier Mantas, Javier G. Castellano, Joaquín Abellán:
Ensemble of classifier chains and Credal C4.5 for solving multi-label classification. Prog. Artif. Intell. 8(2): 195-213 (2019) - [j23]Carlos Javier Mantas, Javier G. Castellano
A comparison of random forest based algorithms: random credal random forest versus oblique random forest. Soft Comput. 23(21): 10739-10754 (2019) - [p1]Joaquín Abellán, Carlos Javier Mantas, Éloi Bossé:
Basic Properties for Total Uncertainty Measures in the Theory of Evidence. Information Quality in Information Fusion and Decision Making 2019: 99-108 - 2018
- [j22]Joaquín Abellán, Carlos Javier Mantas, Javier G. Castellano:
AdaptativeCC4.5: Credal C4.5 with a rough class noise estimator. Expert Syst. Appl. 92: 363-379 (2018) - [j21]Joaquín Abellán, Carlos Javier Mantas, Javier G. Castellano, Serafín Moral:
Increasing diversity in random forest learning algorithm via imprecise probabilities. Expert Syst. Appl. 97: 228-243 (2018) - [j20]Serafín Moral-García, Carlos Javier Mantas, Javier G. Castellano
Using Credal-C4.5 with Binary Relevance for Multi-Label Classification. J. Intell. Fuzzy Syst. 35(6): 6501-6512 (2018) - [c8]Carlos Javier Mantas, Joaquín Abellán, Javier G. Castellano, José Ramón Cano, Serafín Moral:
Credal C4.5 with Refinement of Parameters. IPMU (3) 2018: 739-747 - 2017
- [j19]Joaquín Abellán
A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees. Complex. 2017: 9023970:1-9023970:17 (2017) - [j18]Joaquín Abellán, Carlos Javier Mantas, Javier G. Castellano:
A Random Forest approach using imprecise probabilities. Knowl. Based Syst. 134: 72-84 (2017) - 2016
- [j17]Carlos Javier Mantas, Joaquín Abellán, Javier G. Castellano
Analysis of Credal-C4.5 for classification in noisy domains. Expert Syst. Appl. 61: 314-326 (2016) - 2014
- [j16]Carlos Javier Mantas, Joaquín Abellán:
Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data. Expert Syst. Appl. 41(5): 2514-2525 (2014) - [j15]Joaquín Abellán, Carlos Javier Mantas:
Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Syst. Appl. 41(8): 3825-3830 (2014) - [j14]Carlos Javier Mantas, Joaquín Abellán:
Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data. Expert Syst. Appl. 41(10): 4625-4637 (2014) - [c7]Carlos Javier Mantas, Joaquín Abellán:
Credal decision trees in noisy domains. ESANN 2014 - [c6]Carlos Javier Mantas, Joaquín Abellán:
Credal Decision Trees to Classify Noisy Data Sets. HAIS 2014: 689-696 - [c5]Griselda López
Using Imprecise Probabilities to Extract Decision Rules via Decision Trees for Analysis of Traffic Accidents. RSCTC 2014: 288-298
2000 – 2009
- 2008
- [j13]Carlos Javier Mantas:
A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks. Soft Comput. 12(5): 493-514 (2008) - [j12]Carlos Javier Mantas, José Manuel Puche:
Artificial Neural Networks are Zero-Order TSK Fuzzy Systems. IEEE Trans. Fuzzy Syst. 16(3): 630-643 (2008) - 2007
- [j11]Juan Luis Castro
Extraction of fuzzy rules from support vector machines. Fuzzy Sets Syst. 158(18): 2057-2077 (2007) - 2006
- [j10]Carlos Javier Mantas, José Manuel Puche, José Miguel Mantas
Extraction of similarity based fuzzy rules from artificial neural networks. Int. J. Approx. Reason. 43(2): 202-221 (2006) - [c4]José Manuel Puche, José Manuel Benítez, Juan Luis Castro
Fuzzy Pairwise Multiclass Support Vector Machines. MICAI 2006: 562-571 - 2005
- [c3]Carlos Javier Mantas:
T-norms and t-conorms in multilayer perceptrons. EUSFLAT Conf. 2005: 1313-1318 - [c2]Juan Luis Castro, L. D. Flores-Hidalgo, Carlos Javier Mantas:
Interpretation of Support Vector Machines by means of Fuzzy Rule-Based Systems. EUSFLAT Conf. 2005: 1319-1324 - 2002
- [j9]Carlos Javier Mantas, José Miguel Mantas Ruiz
A procedure for improving generalization in classification trees. Neurocomputing 48(1-4): 727-740 (2002) - [j8]Juan Luis Castro
Interpretation of artificial neural networks by means of fuzzy rules. IEEE Trans. Neural Networks 13(1): 101-116 (2002) - 2001
- [j7]Juan Luis Castro
A fuzzy rule-based algorithm to train perceptrons. Fuzzy Sets Syst. 118(2): 359-367 (2001) - [j6]Juan Luis Castro
A new approach for the execution and adjustment of a fuzzy algorithm. Fuzzy Sets Syst. 121(3): 491-503 (2001) - 2000
- [j5]J. L. Castro, Miguel Delgado, Carlos Javier Mantas:
MORSE: A general model to represent structured knowledge. Int. J. Intell. Syst. 15(1): 27-43 (2000) - [j4]Juan Luis Castro
Neural networks with a continuous squashing function in the output are universal approximators. Neural Networks 13(6): 561-563 (2000) - [j3]Juan Luis Castro Peña
SEPARATE: a machine learning method based on semi-global partitions. IEEE Trans. Neural Networks Learn. Syst. 11(3): 710-720 (2000)
1990 – 1999
- 1999
- [j2]Juan Luis Castro, Miguel Delgado, Carlos Javier Mantas:
Fuzzy Grammar for Handling Fuzzy Algorithms. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 7(3): 277-286 (1999) - [j1]Miguel Delgado, Carlos Javier Mantas, Claudio Moraga:
A Fuzzy Rule Based Backpropagation Method for Training Binary Multilayer Perceptrons. Inf. Sci. 113(1-2): 1-17 (1999) - [c1]José Manuel Benítez, Juan Luis Castro, Carlos Javier Mantas:
A link-pruning algorithm for neural networks. EUSFLAT-ESTYLF Joint Conf. 1999: 473-476
Coauthor Index
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