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ORCIDRicardo B. C. Prudêncio
Ricardo Bastos Cavalcante Prudêncio
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- affiliation: Federal University of Pernambuco, Recife, Brazil
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2020 – today
- 2024
[j32]Patrícia Drapal, Ricardo B. C. Prudêncio, Telmo de Menezes e Silva Filho:
Towards explainable evaluation: Explaining predicted performance using local performance regions. Appl. Soft Comput. 167: 112351 (2024)- [j31]Ana Carolina Lorena
, Pedro Yuri Arbs Paiva, Ricardo B. C. Prudêncio:
Trusting My Predictions: On the Value of Instance-Level Analysis. ACM Comput. Surv. 56(7): 167:1-167:28 (2024) - [c83]Patrícia Drapal, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio:
Meta-Learning and Novelty Detection for Machine Learning with Reject Option. IJCNN 2024: 1-8 - [c82]Eduardo Vargas Ferreira, Ricardo Bastos Cavalcante Prudêncio, Ana Carolina Lorena:
Measuring Latent Traits of Instance Hardness and Classifier Ability using Boltzmann Machines. IJCNN 2024: 1-8 - [c81]Ricardo B. C. Prudêncio, Ana Carolina Lorena, Telmo de Menezes e Silva Filho, Patrícia Drapal, Maria Gabriela Valeriano:
Assessor Models for Explaining Instance Hardness in Classification Problems. IJCNN 2024: 1-8 - 2023
- [c80]Daniel C. Da Costa, Ricardo B. C. Prudêncio, Alexandre Mota:
Assessor Models with a Reject Option for Soccer Result Prediction. ICMLA 2023: 1200-1205 - [c79]Lidia Perside Gomes Nascimento, Ricardo Bastos Cavalcante Prudêncio, Alexandre Cabral Mota, Audir de Araujo Paiva Filho, Pedro Henrique Alves Cruz, Daniel Cardoso Coelho Alves de Oliveira, Pedro Roncoli Sarmet Moreira:
Machine Learning Techniques for Escaped Defect Analysis in Software Testing. SAST 2023: 47-53 - [i7]Manuel Ferreira Junior, Jessica T. S. Reinaldo, Telmo de Menezes e Silva Filho, Eufrasio A. Lima Neto, Ricardo B. C. Prudêncio:
β4-IRT: A New β3-IRT with Enhanced Discrimination Estimation. CoRR abs/2303.17731 (2023) - 2022
- [j30]Kecia Gomes de Moura, Ricardo B. C. Prudêncio, George D. C. Cavalcanti:
Label noise detection under the noise at random model with ensemble filters. Intell. Data Anal. 26(5): 1119-1138 (2022) - [j29]João V. C. Moraes, Jessica T. S. Reinaldo, Manuel Ferreira Junior, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio:
Evaluating regression algorithms at the instance level using item response theory. Knowl. Based Syst. 240: 108076 (2022) - [j28]Chaina Santos Oliveira, João V. C. Moraes, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio:
A two-level Item Response Theory model to evaluate speech synthesis and recognition. Speech Commun. 137: 19-34 (2022) - [c78]Lucas Felipe Ferraro Cardoso, José de S. Ribeiro, Vitor Cirilo Araujo Santos, Raíssa Lorena Silva da Silva, Marcelle Pereira Mota, Ricardo B. C. Prudêncio, Ronnie Cley de Oliveira Alves:
Explanation-by-Example Based on Item Response Theory. BRACIS (1) 2022: 283-297 - [c77]Ricardo B. C. Prudêncio, Telmo de Menezes e Silva Filho:
Explaining Learning Performance with Local Performance Regions and Maximally Relevant Meta-Rules. BRACIS (1) 2022: 550-564 - [c76]Thiago B. R. Silva, Nina I. Verslype, André C. A. Nascimento, Ricardo B. C. Prudêncio:
Predicting Compatibility of Cultivars in Grafting Processes Using Kernel Methods and Collaborative Filtering. BRACIS (1) 2022: 611-625 - [c75]Chaina Oliveira, Ricardo B. C. Prudêncio:
Item Response Theory to Evaluate Speech Synthesis: Beyond Synthetic Speech Difficulty. EBeM@IJCAI 2022 - [c74]Patrícia Drapal, Jullya Clemente, Dailys Maite Aliaga Reyes, Starch Melo de Souza, Anthony Lins, Ricardo B. C. Prudêncio:
A Clustering-Based Method to Anomaly Detection in Thermal Power Plants. IJCNN 2022: 1-7 - [i6]Lucas Felipe Ferraro Cardoso, José de S. Ribeiro, Vitor Cirilo Araujo Santos, Raíssa Lorena Silva da Silva, Marcelle Pereira Mota, Ricardo B. C. Prudêncio, Ronnie Cley de Oliveira Alves:
Explanation-by-Example Based on Item Response Theory. CoRR abs/2210.01638 (2022) - 2021
- [j27]Lucas V. Dias, Péricles B. C. de Miranda, André C. A. Nascimento, Filipe R. Cordeiro, Rafael Ferreira Mello, Ricardo B. C. Prudêncio:
ImageDataset2Vec: An image dataset embedding for algorithm selection. Expert Syst. Appl. 180: 115053 (2021) - [c73]Mailton Carvalho, Flávia A. Barros, Ricardo B. C. Prudêncio:
A Process for Building Domain Specific Thesauri for Query Expansion to Mine SW Documents Repositories within an Industrial Environment. SBES 2021: 21-26 - [i5]Lucas Felipe Ferraro Cardoso, Vitor Cirilo Araujo Santos, Regiane S. Kawasaki Francês, Ricardo B. C. Prudêncio, Ronnie Cley de Oliveira Alves:
Data vs classifiers, who wins? CoRR abs/2107.07451 (2021) - [i4]Tarsicio Lucas, Teresa Bernarda Ludermir, Ricardo B. C. Prudêncio, Carlos Soares:
Meta-aprendizado para otimizacao de parametros de redes neurais. CoRR abs/2109.13745 (2021) - [i3]Kecia Gomes de Moura, Ricardo B. C. Prudêncio, George D. C. Cavalcanti:
Label noise detection under the Noise at Random model with ensemble filters. CoRR abs/2112.01617 (2021) - 2020
- [j26]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio:
A novel context-free grammar for the generation of PSO algorithms. Nat. Comput. 19(3): 495-513 (2020) - [c72]Lucas Felipe Ferraro Cardoso, Vitor Cirilo Araujo Santos, Regiane S. K. Francês, Ricardo B. C. Prudêncio, Ronnie Cley de Oliveira Alves:
Decoding Machine Learning Benchmarks. BRACIS (2) 2020: 412-425 - [c71]José L. M. Arruda, Ricardo B. C. Prudêncio, Ana Carolina Lorena:
Measuring Instance Hardness Using Data Complexity Measures. BRACIS (2) 2020: 483-497 - [c70]Thiago R. Fraça, Péricles B. C. de Miranda, Ricardo B. C. Prudêncio, Ana C. Lorenaz, André C. A. Nascimento:
A Many-Objective optimization Approach for Complexity-based Data set Generation. CEC 2020: 1-8 - [c69]Chaina Santos Oliveira, Caio C. A. Tenório, Ricardo B. C. Prudêncio:
Item Response Theory to Estimate the Latent Ability of Speech Synthesizers. ECAI 2020: 1874-1880 - [c68]João V. C. Moraes, Jessica T. S. Reinaldo, Ricardo B. C. Prudêncio, Telmo de Menezes e Silva Filho:
Item Response Theory for Evaluating Regression Algorithms. IJCNN 2020: 1-8 - [c67]Regina R. Parente, Ricardo B. C. Prudêncio:
One-Class Classification for Selecting Synthetic Datasets in Meta-Learning. IJCNN 2020: 1-8 - [i2]Lucas Felipe Ferraro Cardoso, Vitor Cirilo Araujo Santos, Regiane S. K. Francês, Ricardo B. C. Prudêncio, Ronnie Cley de Oliveira Alves:
Decoding machine learning benchmarks. CoRR abs/2007.14870 (2020)
2010 – 2019
- 2019
- [j25]Fernando Martínez-Plumed, Ricardo B. C. Prudêncio, Adolfo Martínez Usó, José Hernández-Orallo:
Item response theory in AI: Analysing machine learning classifiers at the instance level. Artif. Intell. 271: 18-42 (2019) - [j24]Douglas Veras da Silva, Ricardo B. C. Prudêncio, Carlos Ferraz:
CD-CARS: Cross-domain context-aware recommender systems. Expert Syst. Appl. 135: 388-409 (2019) - [j23]Davi Pereira dos Santos, Ricardo Bastos Cavalcante Prudêncio, André Carlos Ponce de Leon Ferreira de Carvalho:
Empirical investigation of active learning strategies. Neurocomputing 326-327: 15-27 (2019) - [c66]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
$β^3$-IRT: A New Item Response Model and its Applications. AISTATS 2019: 1013-1021 - [c65]Tarcísio Lucas, João Gomes, Renato Vimieiro, Ricardo B. C. Prudêncio, Teresa Bernarda Ludermir:
A Multivariate Method for Group Profiling Using Subgroup Discovery. BRACIS 2019: 371-376 - [c64]Ricardo B. C. Prudêncio:
Cost Sensitive Evaluation of Instance Hardness in Machine Learning. ECML/PKDD (2) 2019: 86-102 - [i1]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
β3-IRT: A New Item Response Model and its Applications. CoRR abs/1903.04016 (2019) - 2018
- [j22]Ana Carolina Lorena, Aron I. Maciel, Péricles B. C. de Miranda, Ivan G. Costa, Ricardo B. C. Prudêncio:
Data complexity meta-features for regression problems. Mach. Learn. 107(1): 209-246 (2018) - [j21]João Emanoel Ambrósio Gomes, Ricardo B. C. Prudêncio, André C. A. Nascimento:
Centrality-Based Group Profiling: A Comparative Study in Co-authorship Networks. New Gener. Comput. 36(1): 59-89 (2018) - [c63]Miguel Domingos de Santana Wanderley, Ricardo Bastos Cavalcante Prudêncio:
Increasing Convolutional Neural Networks Training Speed by Incremental Complexity Learning. BRACIS 2018: 103-108 - [c62]Kecia Gomes de Moura, Ricardo Bastos Cavalcante Prudêncio, George Darmiton da Cunha Cavalcanti:
Ensemble Methods for Label Noise Detection Under the Noisy at Random Model. BRACIS 2018: 474-479 - [c61]Alysson Bispo, Ricardo B. C. Prudêncio, Douglas Veras da Silva:
Instance Selection and Class Balancing Techniques for Cross Project Defect Prediction. BRACIS 2018: 552-557 - [c60]Miguel D. de S. Wanderley, Ricardo B. C. Prudêncio:
Transferring Knowledge From Texts to Images by Combining Deep Semantic Feature Descriptors. IJCNN 2018: 1-7 - [c59]Ivan Santos, Joelson Araújo, Cloves Lima, Ricardo B. C. Prudêncio, Flávia A. Barros:
AVS: An approach to identifying and mitigating duplicate bug reports. SBSI 2018: 22:1-22:7 - 2017
- [j20]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio:
Generation of Particle Swarm Optimization algorithms: An experimental study using Grammar-Guided Genetic Programming. Appl. Soft Comput. 60: 281-296 (2017) - [j19]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio, Gisele L. Pappa:
H3AD: A hybrid hyper-heuristic for algorithm design. Inf. Sci. 414: 340-354 (2017) - [c58]Diana C. Cavalcanti, Ricardo B. C. Prudêncio:
Aspect-Based Opinion Mining in Drug Reviews. EPIA 2017: 815-827 - [c57]Diana C. Cavalcanti, Ricardo B. C. Prudêncio:
Unsupervised Aspect Term Extraction in Online Drugs Reviews. FLAIRS 2017: 38-43 - 2016
- [j18]José Hernández-Orallo, Adolfo Martínez Usó, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach, Chowdhury Farhan Ahmed, Nicolas Lachiche:
Reframing in context: A systematic approach for model reuse in machine learning. AI Commun. 29(5): 551-566 (2016) - [j17]André C. A. Nascimento, Ricardo B. C. Prudêncio, Ivan G. Costa:
A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinform. 17: 46 (2016) - [j16]Ricardo B. C. Prudêncio, Teresa Bernarda Ludermir:
Progress in intelligent systems design. Neurocomputing 180: 1-2 (2016) - [j15]Arthur F. M. Sousa, Ricardo B. C. Prudêncio, Teresa Bernarda Ludermir, Carlos Soares:
Active learning and data manipulation techniques for generating training examples in meta-learning. Neurocomputing 194: 45-55 (2016) - [j14]Telmo de Menezes e Silva Filho, Renata M. C. R. Souza, Ricardo B. C. Prudêncio:
A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data. Neural Networks 80: 19-33 (2016) - [c56]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio:
Tree-Based Grammar Genetic Programming to Evolve Particle Swarm Algorithms. BRACIS 2016: 25-30 - [c55]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio:
A Novel Context-Free Grammar to Guide the Construction of Particle Swarm Optimization Algorithms. BRACIS 2016: 295-300 - [c54]João Emanoel Ambrósio Gomes, Ricardo B. C. Prudêncio, André C. A. Nascimento:
A Comparative Study of Group Profiling Techniques in Co-authorship Networks. BRACIS 2016: 373-378 - [c53]Fernando Martínez-Plumed, Ricardo B. C. Prudêncio, Adolfo Martínez Usó, José Hernández-Orallo:
Making Sense of Item Response Theory in Machine Learning. ECAI 2016: 1140-1148 - 2015
- [j13]Douglas Veras da Silva, Thiago Monteiro Prota, Alysson Bispo, Ricardo B. C. Prudêncio, Carlos Ferraz:
A literature review of recommender systems in the television domain. Expert Syst. Appl. 42(22): 9046-9076 (2015) - [j12]Luciano S. de Souza, Ricardo Bastos Cavalcante Prudêncio, Flávia A. Barros:
A hybrid particle swarm optimization and harmony search algorithm approach for multi-objective test case selection. J. Braz. Comput. Soc. 21(1): 19:1-19:20 (2015) - [c52]Danilo C. G. de Lucena, Ricardo B. C. Prudêncio:
Semi-supervised Multi-label k-Nearest Neighbors Classification Algorithms. BRACIS 2015: 49-54 - [c51]Douglas Veras da Silva, Ricardo B. C. Prudêncio, Carlos Ferraz, Alysson Bispo, Thiago Monteiro Prota:
Context-Aware Techniques for Cross-Domain Recommender Systems. BRACIS 2015: 282-287 - [c50]Péricles B. C. de Miranda, Paulo Ricardo da Silva Soares, Ricardo B. C. Prudêncio:
I/S-Race: An iterative Multi-Objective Racing Algorithm for the SVM Parameter Selection Problem. ESANN 2015 - [c49]Péricles Barbosa C. de Miranda, Ricardo Bastos Cavalcante Prudêncio:
GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution. GECCO 2015: 1087-1094 - [c48]Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach:
Versatile Decision Trees for Learning Over Multiple Contexts. ECML/PKDD (1) 2015: 184-199 - 2014
- [j11]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio, André C. P. L. F. de Carvalho, Carlos Soares:
A hybrid meta-learning architecture for multi-objective optimization of SVM parameters. Neurocomputing 143: 27-43 (2014) - [c47]Carlos Eduardo Castor de Melo, Ricardo Bastos Cavalcante Prudêncio:
Cost-Sensitive Measures of Algorithm Similarity for Meta-learning. BRACIS 2014: 7-12 - [c46]Luciano S. de Souza, Ricardo B. C. Prudêncio, Flávia de Almeida Barros:
A Hybrid Binary Multi-objective Particle Swarm Optimization with Local Search for Test Case Selection. BRACIS 2014: 414-419 - [c45]Luciano S. de Souza, Ricardo B. C. Prudêncio, Flávia de Almeida Barros:
A comparison study of binary multi-objective Particle Swarm Optimization approaches for test case selection. IEEE Congress on Evolutionary Computation 2014: 2164-2171 - [c44]Carlos Eduardo Castor de Melo, Ricardo B. C. Prudêncio:
Similarity Measures of Algorithm Performance for Cost-Sensitive Scenarios. MetaSel@ECAI 2014: 11-17 - [c43]Péricles B. C. de Miranda, Paulo Ricardo da Silva Soares, Ricardo B. C. Prudêncio:
Fine-tuning of support vector machine parameters using racing algorithms. ESANN 2014 - [c42]Andre R. S. Lopes, Ricardo B. C. Prudêncio, Byron L. D. Bezerra:
A collaborative filtering framework based on local and global similarities with similarity tie-breaking criteria. IJCNN 2014: 2887-2893 - 2013
- [j10]Luciano S. de Souza, Ricardo B. C. Prudêncio, Flávia de Almeida Barros, Eduardo Henrique da Silva Aranha:
Search based constrained test case selection using execution effort. Expert Syst. Appl. 40(12): 4887-4896 (2013) - [j9]Paulo Ricardo da Silva Soares, Ricardo B. C. Prudêncio:
Proximity measures for link prediction based on temporal events. Expert Syst. Appl. 40(16): 6652-6660 (2013) - [c41]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio:
Active testing for SVM parameter selection. IJCNN 2013: 1-8 - [c40]Arthur F. M. Sousa, Ricardo B. C. Prudêncio, Carlos Soares, Teresa Bernarda Ludermir:
Active selection of training instances for a random forest meta-learner. IJCNN 2013: 1-7 - [c39]João Gomes, Ricardo B. C. Prudêncio, Luciano Meira, Alexandre Azevedo Filho, André C. A. Nascimento, Hilário Oliveira:
Group Profiling for Understanding Educational Social Networking. SEKE 2013: 101-106 - 2012
- [j8]Taciana A. F. Gomes, Ricardo Bastos Cavalcante Prudêncio, Carlos Soares, André L. D. Rossi, André C. P. L. F. de Carvalho:
Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 75(1): 3-13 (2012) - [j7]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Combining Uncertainty Sampling methods for supporting the generation of meta-examples. Inf. Sci. 196: 1-14 (2012) - [c38]Péricles B. C. de Miranda, Ricardo Bastos Cavalcante Prudêncio, André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares:
An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection. ICCSA (3) 2012: 562-575 - [c37]Juliano C. B. Rabelo, Ricardo B. C. Prudêncio, Flávia A. Barros:
Collective Classification for Sentiment Analysis in Social Networks. ICTAI 2012: 958-963 - [c36]Péricles B. C. de Miranda, Ricardo Bastos Cavalcante Prudêncio, André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares:
Multi-objective optimization and Meta-learning for SVM parameter selection. IJCNN 2012: 1-8 - [c35]Paulo Ricardo da Silva Soares, Ricardo Bastos Cavalcante Prudêncio:
Time Series Based Link Prediction. IJCNN 2012: 1-7 - [c34]Péricles B. C. de Miranda, Ricardo B. C. Prudêncio, André C. P. L. F. de Carvalho, Carlos Soares:
Combining Meta-Learning with Multi-objective Particle Swarm Algorithms for SVM Parameter Selection: An Experimental Analysis. SBRN 2012: 1-6 - [c33]Juliano C. B. Rabelo, Ricardo B. C. Prudêncio, Flávia de Almeida Barros:
Using link structure to infer opinions in social networks. SMC 2012: 681-685 - [c32]Péricles B. C. de Miranda, Ricardo Bastos Cavalcante Prudêncio, André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares:
Combining a multi-objective optimization approach with meta-learning for SVM parameter selection. SMC 2012: 2909-2914 - [c31]Juliano C. B. Rabelo, Ricardo B. C. Prudêncio, Flávia A. Barros:
Leveraging relationships in social networks for sentiment analysis. WebMedia 2012: 181-188 - 2011
- [j6]Teresa Bernarda Ludermir, Ricardo Bastos Cavalcante Prudêncio, Cleber Zanchettin:
Feature and algorithm selection with Hybrid Intelligent Techniques. Int. J. Hybrid Intell. Syst. 8(3): 115-116 (2011) - [c30]Ricardo Bastos Cavalcante Prudêncio, Carlos Soares, Teresa Bernarda Ludermir:
Combining Meta-learning and Active Selection of Datasetoids for Algorithm Selection. HAIS (1) 2011: 164-171 - [c29]Ricardo Bastos Cavalcante Prudêncio, Carlos Soares, Teresa Bernarda Ludermir:
Uncertainty Sampling-Based Active Selection of Datasetoids for Meta-learning. ICANN (2) 2011: 454-461 - [c28]Diana C. Cavalcanti, Ricardo Bastos Cavalcante Prudêncio, Shreyasee S. Pradhan, Jatin Shah, Ricardo Pietrobon:
Good to be Bad? Distinguishing between Positive and Negative Citations in Scientific Impact. ICTAI 2011: 156-162 - [c27]Luciano S. de Souza, Péricles B. C. de Miranda, Ricardo Bastos Cavalcante Prudêncio, Flávia de Almeida Barros:
A Multi-objective Particle Swarm Optimization for Test Case Selection Based on Functional Requirements Coverage and Execution Effort. ICTAI 2011: 245-252 - [c26]Ricardo Bastos Cavalcante Prudêncio, Carlos Soares, Teresa Bernarda Ludermir:
Uncertainty sampling methods for selecting datasets in active meta-learning. IJCNN 2011: 1082-1089 - [c25]Hially Rodrigues de Sa, Ricardo Bastos Cavalcante Prudêncio:
Supervised link prediction in weighted networks. IJCNN 2011: 2281-2288 - [p1]Ricardo Bastos Cavalcante Prudêncio, Marcilio C. P. de Souto, Teresa Bernarda Ludermir:
Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach. Meta-Learning in Computational Intelligence 2011: 225-243 - 2010
- [j5]Mitsuo Takaki, Diego Cavalcanti, Rohit Gheyi, Juliano Iyoda, Marcelo d'Amorim, Ricardo Bastos Cavalcante Prudêncio:
Randomized constraint solvers: a comparative study. Innov. Syst. Softw. Eng. 6(3): 243-253 (2010) - [c24]Marcelo Nunes Ribeiro, Ricardo Bastos Cavalcante Prudêncio:
Local Feature Selection for Generation of Ensembles in Text Clustering. SBRN 2010: 67-72 - [c23]Taciana A. F. Gomes, Ricardo Bastos Cavalcante Prudêncio, Carlos Soares, André L. D. Rossi, André Carlos Ponce de Leon Ferreira de Carvalho:
Combining Meta-learning and Search Techniques to SVM Parameter Selection. SBRN 2010: 79-84 - [c22]Luciano S. de Souza, Ricardo Bastos Cavalcante Prudêncio, Flávia de Almeida Barros:
A Constrained Particle Swarm Optimization Approach for Test Case Selection. SEKE 2010: 259-264
2000 – 2009
- 2009
- [j4]Flávia A. Barros, Eduardo F. A. Silva, Ricardo Bastos Cavalcante Prudêncio, Valmir M. Filho, André C. A. Nascimento:
Combining Text Classifiers and Hidden Markov Models for Information Extraction. Int. J. Artif. Intell. Tools 18(2): 311-329 (2009) - [c21]André C. A. Nascimento, Ricardo Bastos Cavalcante Prudêncio, Marcílio Carlos Pereira de Souto, Ivan G. Costa:
Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data. ICANN (2) 2009: 20-29 - [c20]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Active Generation of Training Examples in Meta-Regression. ICANN (1) 2009: 30-39 - [c19]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Combining Uncertainty Sampling Methods for Active Meta-Learning. ISDA 2009: 220-225 - [c18]Mitsuo Takaki, Diego Cavalcanti, Rohit Gheyi, Juliano Iyoda, Marcelo d'Amorim, Ricardo Bastos Cavalcante Prudêncio:
A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing. NASA Formal Methods 2009: 56-65 - 2008
- [j3]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Selective generation of training examples in active meta-learning. Int. J. Hybrid Intell. Syst. 5(2): 59-70 (2008) - [c17]Flávia A. Barros, Eduardo F. A. Silva, Ricardo Bastos Cavalcante Prudêncio, Valmir M. Filho, André C. A. Nascimento:
Hidden Markov Models and Text Classifiers for Information Extraction on Semi-Structured Texts. HIS 2008: 417-422 - [c16]Silvio B. Guerra, Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors. ICANN (1) 2008: 523-532 - [c15]Marcelo Nunes Ribeiro, Manoel J. R. Neto, Ricardo Bastos Cavalcante Prudêncio:
Local Feature Selection in Text Clustering. ICONIP (2) 2008: 45-52 - [c14]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Active Meta-Learning with Uncertainty Sampling and Outlier Detection. IJCNN 2008: 346-351 - [c13]Marcílio Carlos Pereira de Souto, Ricardo Bastos Cavalcante Prudêncio, Rodrigo G. F. Soares, Daniel S. A. de Araujo, Ivan G. Costa, Teresa Bernarda Ludermir, Alexander Schliep:
Ranking and selecting clustering algorithms using a meta-learning approach. IJCNN 2008: 3729-3735 - [c12]Valmir Macário Filho, Ricardo Bastos Cavalcante Prudêncio, Francisco de A. T. de Carvalho, Leandro R. Torres, Laerte Rodrigues Jr., Marcos G. Lima:
Automatic Information Extraction in Semi-structured Official Journals. SBRN 2008: 51-56 - [c11]Patrícia M. Santos, Teresa Bernarda Ludermir, Ricardo Bastos Cavalcante Prudêncio:
Selecting Neural Network Forecasting Models Using the Zoomed-Ranking Approach. SBRN 2008: 165-170 - [c10]Ricardo Bastos Cavalcante Prudêncio, Silvio B. Guerra, Teresa Bernarda Ludermir:
Using Support Vector Machines to Predict the Performance of MLP Neural Networks. SBRN 2008: 201-206 - 2007
- [c9]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Active Selection of Training Examples for Meta-Learning. HIS 2007: 126-131 - [c8]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir:
Active Learning to Support the Generation of Meta-examples. ICANN (1) 2007: 817-826 - 2006
- [c7]