Paulo J. L. Adeodato - researchr alias

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2022

A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessmentPaulo J. L. Adeodato, Sílvio B. Melo. ijcnn 2022: 1-6 [doi]

Kolmogorov-Smirnov and ROC curve metrics for binary classification performance assessment are equivalentPaulo J. L. Adeodato, Sílvio B. Melo. icpr 2022: 1194-1199 [doi]

Please stop trying to predict elections only with TwitterKellyton Brito, Rogério Luiz Cardoso Silva Filho, Paulo J. L. Adeodato. DG.O 2022: 88-95 [doi]

PAN RAM Bootstrapping Regressor - A New RAM-Based Architecture for Regression ProblemsStarch Melo de Souza, Kelly Pereira de Lima, Anthony José da Cunha Carneiro Lins, Adriano Fabio Querino de Brito, Paulo J. L. Adeodato. bracis 2022: 574-587 [doi]

Using data mining over open data for a longitudinal assessment of municipal public education in BrazilArthur Scanoni, Rogério L. C. Silva Filho, Paulo J. L. Adeodato, Kellyton Brito. egov 2023: [doi]

2020

Where to aim? Factors that influence the performance of Brazilian secondary schoolsPaulo J. L. Adeodato, Rogerio Luiz C. S. Filho. edm 2020: [doi]

2019

Data Mining Solution for Assessing the Secondary School Students of Brazilian Federal InstitutesRogério L. C. Silva Filho, Paulo J. L. Adeodato. bracis 2019: 574-579 [doi]

ARENA - Augmented Reality to Enhanced Experimentation in Smart WarehousesLuis Piardi, Vivian Cremer Kalempa, Marcelo Limeira, André Schneider de Oliveira, Paulo J. L. Adeodato. sensors, 19(19):4308, 2019. [doi]

2016

Polynomial approximation RAM neuron capable of handling true continuous input variablesPaulo J. L. Adeodato, Rosalvo F. Oliveira Neto. ijcnn 2016: 76-83 [doi]

Equivalência entre a Área sob a Curva Kolmogorov-Smirnov e o Índice de Gini na Avaliação de Desempenho de Decisões BináriasPaulo J. L. Adeodato, Sílvio B. Melo. sbbd 2016: 157-162 [doi]

2015

Variable Transformation for Granularity Change in Hierarchical Databases in Actual Data Mining SolutionsPaulo J. L. Adeodato. ideal 2015: 146-155 [doi]

2014

Continuous variables segmentation and reordering for optimal performance on binary classification tasksPaulo J. L. Adeodato, Domingos S. P. Salazar, Lucas S. Gallindo, Abner G. Sa, Starch M. Souza. ijcnn 2014: 3720-3725 [doi]

CoMoVi: a Framework for Data Transformation in Credit Behavioral Scoring Applications Using Model Driven ArchitectureRosalvo F. O. Neto, Paulo J. L. Adeodato, Ana Carolina Salgado, Dailton Rodrigues de Carvalho Filho, Genival Rocha Machado. seke 2014: 286-291

2013

A Temporal Difference GNG-Based Algorithm That Can Learn to Control in Reinforcement Learning EnvironmentsDavi C. de L. Vieira, Paulo J. L. Adeodato, Paulo M. Goncalves Junior. icmla 2013: 329-332 [doi]

2012

Data transformations and seasonality adjustments improve forecasts of MLP ensemblesDomingos S. P. Salazar, Paulo J. L. Adeodato, Adrian L. Arnaud. eais 2012: 139-144 [doi]

2011

Predicting software defects: A cost-sensitive approachMiguel E. R. Bezerra, Adriano L. I. Oliveira, Paulo J. L. Adeodato. SMC 2011: 2515-2522 [doi]

PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data setsIcamaan B. Viegas da Silva, Paulo J. L. Adeodato. ijcnn 2011: 2664-2669 [doi]

2010

Domain Driven Data Mining for Unavailability Estimation of Electrical Power GridsPaulo J. L. Adeodato, Petrônio L. Braga, Adrian L. Arnaud, Germano C. Vasconcelos, Frederico Guedes, Hélio B. Menezes, Giorgio O. Limeira. ieaaie 2010: 357-366 [doi]

The Power of Sampling and Stacking for the PAKDD-2007 Cross-Selling ProblemPaulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha. In David Taniar, Laura Irina Rusu, editors, Strategic Advancements in Utilizing Data Mining and Warehousing Technologies: New Concepts and Developments. pages 297-306, IGI Global, 2010. [doi]

pRAM n-tuple Classifier - a new architecture of probabilistic RAM neurons for classification problemsPaulo J. L. Adeodato, Rosalvo F. O. Neto. ijcnn 2010: 1-7 [doi]

Improving reinforcement learning algorithms by the use of data mining techniques for feature and action selectionDavi C. de L. Vieira, Paulo J. L. Adeodato, Paulo M. Goncalves. SMC 2010: 1863-1870 [doi]

Fat Tailed Distribution of Neural Networks ForecastingDomingos S. M. P. Salazar, Maíra de O. Santos, Adrian L. Arnaud, Paulo J. L. Adeodato. dmin 2010: 319-326

An Approach for Learning from Small and Unbalanced Data Sets Using Gaussian Noise During Artificial Neural Network TrainingIcamaan Viegas, Paulo J. L. Adeodato. dmin 2010: 23-30

2009

A Decision Support System Based on Data Mining for Pediatric Cardiology DiagnosisPaulo J. L. Adeodato, TarcÃsio Gurgel, Sandra Mattos. dmin 2009: 138-143

The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification ChallengePaulo J. L. Adeodato, Adrian L. Arnaud, Germano C. Vasconcelos, Rodrigo C. L. V. Cunha, Tarcísio B. Gurgel, Domingos S. M. P. Monteiro. ijcnn 2009: 57-62 [doi]

Integration and Knowledge Reuse Environment for Producing Award Winning Solutions for Binary Decision Data Mining ProblemsRodrigo C. L. V. Cunha, Paulo J. L. Adeodato, Silvio R. L. Meira. iri 2009: 376-381 [doi]

Knowledge Reuse in Data Mining Projects and Its Practical ApplicationsRodrigo C. L. V. Cunha, Paulo J. L. Adeodato, Silvio Romero de Lemos Meira. ICEIS 2009: 317-324 [doi]

A data mining approach to solve the goal scoring problemRenato Oliveira, Paulo J. L. Adeodato, Arthur Carvalho, Icamaan Viegas, Christian Diego, Ing Ren Tsang. ijcnn 2009: 2347-2352 [doi]

2008

A systematic solution for the NN3 Forecasting Competition problem based on an ensemble of MLP neural networksPaulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha, Domingos Sávio Malaquias Pessoa Monteiro. icpr 2008: 1-4 [doi]

The Power of Sampling and Stacking for the PaKDD-2007 Cross-Selling ProblemPaulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha, Domingos S. M. P. Monteiro, Rosalvo F. O. Neto. jdwm, 4(2):22-31, 2008. [doi]

Enhancing RBF-DDA Algorithm s Robustness: Neural Networks Applied to Prediction of Fault-Prone Software ModulesMiguel E. R. Bezerra, Adriano L. I. Oliveira, Paulo J. L. Adeodato, Silvio R. L. Meira. ifip12 2008: 119-128 [doi]

A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural NetworksTiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato. npl, 28(2):113-129, 2008. [doi]

2007

A New Evolutionary Approach for Time Series ForecastingTiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato. cidm 2007: 616-623 [doi]

2005

A new evolutionary method for time series forecastingTiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato. gecco 2005: 2221-2222 [doi]

2004

Neural Networks vs Logistic Regression: a Comparative Study on a Large Data SetPaulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Roberto A. F. Santos, Rodrigo C. L. V. Cunha, Domingos S. M. P. Monteiro. icpr 2004: 355-358 [doi]

A hybrid intelligent system approach for improving the prediction of real world time seriesTiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato. cec 2004: 736-743 [doi]

1999

Sequential RAM-based Neural Networks: Learnability, Generalisation, Knowledge Extraction, and Grammatical InferenceMarcílio Carlos Pereira de Souto, Paulo J. L. Adeodato, Teresa Bernarda Ludermir. IJNS, 9(3):203-210, 1999. [doi]

1998

Learnability in Sequential RAM-based Neural NetworksMarcílio Carlos Pereira de Souto, Paulo J. L. Adeodato. sbrn 1998: 20-25 [doi]

1997

Stability analysis of pRAM reinforcement learningPaulo J. L. Adeodato, John G. Taylor. sbrn 1997: 41-50 [doi]

1996

Autoassociative Memory with high Storage CapacityPaulo J. L. Adeodato, John G. Taylor. icann 1996: 29-34

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