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ORCIDPaulo J. L. Adeodato
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- affiliation: Universidade Federal de Pernambuco, Brasil
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2020 – today
- 2023
[j10]Rogério Luiz Cardoso Silva Filho
, Kellyton Brito, Paulo Jorge Leitão Adeodato:
A data mining framework for reporting trends in the predictive contribution of factors related to educational achievement. Expert Syst. Appl. 221: 119729 (2023)- [j9]Kellyton Brito, Paulo Jorge Leitão Adeodato:
Machine learning for predicting elections in Latin America based on social media engagement and polls. Gov. Inf. Q. 40(1): 101782 (2023) - 2022
- [j8]Kellyton Brito, Paulo Jorge Leitão Adeodato:
Measuring the performances of politicians on social media and the correlation with major Latin American election results. Gov. Inf. Q. 39(4): 101745 (2022) - [c41]Starch Melo de Souza, Kelly Pereira de Lima, Anthony José da Cunha Carneiro Lins, Adriano Fabio Querino de Brito, Paulo J. L. Adeodato:
PAN RAM Bootstrapping Regressor - A New RAM-Based Architecture for Regression Problems. BRACIS (2) 2022: 574-587 - [c40]Kellyton Brito, Rogério Luiz Cardoso Silva Filho, Paulo J. L. Adeodato:
Please stop trying to predict elections only with Twitter. DG.O 2022: 88-95 - [c39]Arthur Scanoni, Rogério L. C. Silva Filho, Paulo J. L. Adeodato, Kellyton Brito:
Using data mining over open data for a longitudinal assessment of municipal public education in Brazil. EGOV-CeDEM-ePart-* 2022 - [c38]Paulo J. L. Adeodato, Sílvio B. Melo:
Kolmogorov-Smirnov and ROC curve metrics for binary classification performance assessment are equivalent. ICPR 2022: 1194-1199 - [c37]Paulo J. L. Adeodato, Sílvio B. Melo:
A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessment. IJCNN 2022: 1-6 - 2021
- [j7]Kellyton dos Santos Brito, Silvio Romero de Lemos Meira, Paulo Jorge Leitão Adeodato:
Correlations of social media performance and electoral results in Brazilian presidential elections. Inf. Polity 26(4): 417-439 (2021) - [j6]Kellyton dos Santos Brito, Rogério Luiz Cardoso Silva Filho, Paulo Jorge Leitão Adeodato:
A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions. IEEE Trans. Comput. Soc. Syst. 8(4): 819-843 (2021) - [c36]Rogério Luiz Cardoso Silva Filho, Paulo Jorge Leitão Adeodato, Kellyton dos Santos Brito:
Interpreting Classification Models Using Feature Importance Based on Marginal Local Effects. BRACIS (1) 2021: 484-497 - 2020
- [c35]Paulo J. L. Adeodato, Rogerio Luiz C. S. Filho:
Where to aim? Factors that influence the performance of Brazilian secondary schools. EDM 2020 - [c34]Kellyton dos Santos Brito, Paulo Jorge Leitão Adeodato:
Predicting Brazilian and U.S. Elections with Machine Learning and Social Media Data. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [c33]Rogério L. C. Silva Filho, Paulo J. L. Adeodato:
Data Mining Solution for Assessing the Secondary School Students of Brazilian Federal Institutes. BRACIS 2019: 574-579 - 2017
- [j5]Rosalvo F. O. Neto, Paulo Jorge Leitão Adeodato, Ana Carolina Salgado:
A framework for data transformation in Credit Behavioral Scoring applications based on Model Driven Development. Expert Syst. Appl. 72: 293-305 (2017) - [i3]Paulo J. L. Adeodato, Fábio C. Pereira, Rosalvo F. Oliveira Neto:
Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining. CoRR abs/1702.08745 (2017) - 2016
- [c32]Paulo J. L. Adeodato, Rosalvo F. Oliveira Neto:
Polynomial approximation RAM neuron capable of handling true continuous input variables. IJCNN 2016: 76-83 - [c31]Paulo J. L. Adeodato, Sílvio B. Melo:
Equivalência entre a Área sob a Curva Kolmogorov-Smirnov e o Índice de Gini na Avaliação de Desempenho de Decisões Binárias. SBBD 2016: 157-162 - [i2]Paulo J. L. Adeodato, Sílvio B. Melo:
On the equivalence between Kolmogorov-Smirnov and ROC curve metrics for binary classification. CoRR abs/1606.00496 (2016) - 2015
- [c30]Paulo J. L. Adeodato:
Variable Transformation for Granularity Change in Hierarchical Databases in Actual Data Mining Solutions. IDEAL 2015: 146-155 - 2014
- [j4]Domingos Savio Pereira Salazar, Paulo Jorge Leitão Adeodato, Adrian Lucena Arnaud:
Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series. IEEE Trans. Neural Networks Learn. Syst. 25(1): 241-246 (2014) - [c29]Paulo J. L. Adeodato, Domingos S. P. Salazar, Lucas S. Gallindo, Abner G. Sa, Starch Melo de Souza:
Continuous variables segmentation and reordering for optimal performance on binary classification tasks. IJCNN 2014: 3720-3725 - [c28]Rosalvo F. O. Neto, Paulo J. L. Adeodato, Ana Carolina Salgado, Dailton Rodrigues de Carvalho Filho, Genival Rocha Machado:
CoMoVi: a Framework for Data Transformation in Credit Behavioral Scoring Applications Using Model Driven Architecture. SEKE 2014: 286-291 - 2013
- [c27]Flávio H. D. Araújo, Lailson B. Moraes, André M. Santana, Pedro de A. Santos Neto, Paulo J. L. Adeodato, Érico Leão:
Evaluation of the use of computational intelligence techniques in medical claim processes of a health insurance company. CBMS 2013: 23-28 - [c26]Davi C. de L. Vieira, Paulo J. L. Adeodato, Paulo M. Goncalves Junior:
A Temporal Difference GNG-Based Algorithm That Can Learn to Control in Reinforcement Learning Environments. ICMLA (1) 2013: 329-332 - [c25]Davi Carnauba de Lima Vieira, Paulo Jorge Leitão Adeodato, Paulo M. Goncalves:
A Temporal Difference GNG-Based Approach for the State Space Quantization in Reinforcement Learning Environments. ICTAI 2013: 561-568 - [i1]Renato Oliveira, Paulo J. L. Adeodato, Arthur Carvalho, Icamaan Viegas, Christian Diego, Tsang Ing Ren:
A Data Mining Approach to Solve the Goal Scoring Problem. CoRR abs/1305.4955 (2013) - 2012
- [c24]Domingos S. P. Salazar, Paulo J. L. Adeodato, Adrian L. Arnaud:
Data transformations and seasonality adjustments improve forecasts of MLP ensembles. EAIS 2012: 139-144 - [c23]Hadautho Roberto Barros da Silva, Paulo Jorge Leitão Adeodato:
A data mining approach for preventing undergraduate students retention. IJCNN 2012: 1-8 - [c22]Rosalvo F. O. Neto, Paulo Jorge Leitão Adeodato, Ana Carolina Salgado, Murilo Boratto:
Estudo Comparativo entre Proposicionalização e Mineração de Dados Multidimensional sobre um Banco de Dados Relacional. SBBD (Short Papers) 2012: 240-247 - 2011
- [c21]Icamaan B. Viegas da Silva, Paulo J. L. Adeodato:
PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets. IJCNN 2011: 2664-2669 - [c20]Miguel E. R. Bezerra, Adriano L. I. Oliveira, Paulo J. L. Adeodato:
Predicting software defects: A cost-sensitive approach. SMC 2011: 2515-2522 - 2010
- [c19]Icamaan Viegas, Paulo J. L. Adeodato:
An Approach for Learning from Small and Unbalanced Data Sets Using Gaussian Noise During Artificial Neural Network Training. DMIN 2010: 23-30 - [c18]Domingos S. P. Salazar, Maíra de O. Santos, Adrian L. Arnaud, Paulo J. L. Adeodato:
Fat Tailed Distribution of Neural Networks Forecasting. DMIN 2010: 319-326 - [c17]Paulo J. L. Adeodato, Petrônio L. Braga, Adrian L. Arnaud, Germano C. Vasconcelos, Frederico Guedes, Hélio B. Menezes, Giorgio O. Limeira:
Domain Driven Data Mining for Unavailability Estimation of Electrical Power Grids. IEA/AIE (2) 2010: 357-366 - [c16]Paulo J. L. Adeodato, Rosalvo F. O. Neto:
pRAM n-tuple Classifier - a new architecture of probabilistic RAM neurons for classification problems. IJCNN 2010: 1-7 - [c15]Davi C. de L. Vieira, Paulo J. L. Adeodato, Paulo M. Goncalves:
Improving reinforcement learning algorithms by the use of data mining techniques for feature and action selection. SMC 2010: 1863-1870 - [p1]Paulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha, Domingos S. M. P. Monteiro, Rosalvo F. Oliveira Neto:
The Power of Sampling and Stacking for the PAKDD-2007 Cross-Selling Problem. Strategic Advancements in Utilizing Data Mining and Warehousing Technologies 2010: 297-306 - [e1]Thanaruk Theeramunkong, Cholwich Nattee, Paulo J. L. Adeodato, Nitesh V. Chawla, Peter Christen, Philippe Lenca, Josiah Poon, Graham J. Williams:
New Frontiers in Applied Data Mining, PAKDD 2009 International Workshops, Bangkok, Thailand, April 27-30, 2009. Revised Selected Papers. Lecture Notes in Computer Science 5669, Springer 2010, ISBN 978-3-642-14639-8 [contents]
2000 – 2009
- 2009
- [c14]Paulo J. L. Adeodato, Tarcísio B. Gurgel, Sandra Mattos:
A Decision Support System Based on Data Mining for Pediatric Cardiology Diagnosis. DMIN 2009: 138-143 - [c13]Rodrigo C. L. V. Cunha, Paulo J. L. Adeodato, Silvio Romero de Lemos Meira:
Knowledge Reuse in Data Mining Projects and Its Practical Applications. ICEIS 2009: 317-324 - [c12]Paulo J. L. Adeodato, Adrian L. Arnaud, Germano C. Vasconcelos, Rodrigo C. L. V. Cunha, Tarcísio B. Gurgel, Domingos S. M. P. Monteiro:
The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification Challenge. IJCNN 2009: 57-62 - [c11]Renato Oliveira, Paulo J. L. Adeodato, Arthur Carvalho, Icamaan Viegas, Christian Diego, Ing Ren Tsang:
A data mining approach to solve the goal scoring problem. IJCNN 2009: 2347-2352 - [c10]Rodrigo C. L. V. Cunha, Paulo J. L. Adeodato, Silvio R. L. Meira:
Integration and Knowledge Reuse Environment for Producing Award Winning Solutions for Binary Decision Data Mining Problems. IRI 2009: 376-381 - 2008
- [j3]Paulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha, Domingos S. M. P. Monteiro, Rosalvo F. O. Neto:
The Power of Sampling and Stacking for the PaKDD-2007 Cross-Selling Problem. Int. J. Data Warehous. Min. 4(2): 22-31 (2008) - [j2]Tiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato:
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks. Neural Process. Lett. 28(2): 113-129 (2008) - [c9]Paulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha, Domingos Sávio Malaquias Pessoa Monteiro:
A systematic solution for the NN3 Forecasting Competition problem based on an ensemble of MLP neural networks. ICPR 2008: 1-4 - [c8]Miguel E. R. Bezerra, Adriano L. I. Oliveira, Paulo J. L. Adeodato, Silvio R. L. Meira:
Enhancing RBF-DDA Algorithm's Robustness: Neural Networks Applied to Prediction of Fault-Prone Software Modules. IFIP AI 2008: 119-128 - 2007
- [c7]Tiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato:
A New Evolutionary Approach for Time Series Forecasting. CIDM 2007: 616-623 - 2005
- [c6]Tiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato:
A new evolutionary method for time series forecasting. GECCO 2005: 2221-2222 - 2004
- [c5]Tiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato:
A hybrid intelligent system approach for improving the prediction of real world time series. IEEE Congress on Evolutionary Computation 2004: 736-743 - [c4]Paulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Roberto A. F. Santos, Rodrigo C. L. V. Cunha, Domingos S. M. P. Monteiro:
Neural Networks vs Logistic Regression: a Comparative Study on a Large Data Set. ICPR (3) 2004: 355-358
1990 – 1999
- 1999
- [j1]Marcílio Carlos Pereira de Souto, Paulo J. L. Adeodato, Teresa Bernarda Ludermir:
Sequential RAM-based Neural Networks: Learnability, Generalisation, Knowledge Extraction, and Grammatical Inference. Int. J. Neural Syst. 9(3): 203-210 (1999) - 1998
- [c3]Marcílio Carlos Pereira de Souto, Paulo J. L. Adeodato:
Learnability in Sequential RAM-based Neural Networks. SBRN 1998: 20-25 - 1997
- [c2]Paulo J. L. Adeodato, John G. Taylor:
Stability analysis of pRAM reinforcement learning. SBRN 1997: 41-50 - 1996
- [c1]Paulo J. L. Adeodato, John G. Taylor:
Autoassociative Memory with high Storage Capacity. ICANN 1996: 29-34
Coauthor Index
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last updated on 2024-04-12 18:29 CEST by the dblp team
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