Publicaciones

La mayoría de estas publicaciones se encuentran disponibles en ResearchGate

 Sebastián Maldonado

 Carla Vairetti

Publicaciones 2021

Paper

FW-SMOTE: a feature-weighted oversampling approach for imbalanced classification.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – Pattern Recognition. In press.

Maldonado, S.
Vairetti, C.,
A. Fernandez,
F. Herrera.

Paper

Telecom traffic pumping analytics via explainable data science.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – Decision Support Systems 150, 113559; special issue on interpretable data science for decision making.

Irarrázaval, E.
Maldonado, S.,
Pérez, J.,
Vairetti, C.

Paper

A Transparency Maturity Model for Government Software Tenders.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – IEEE Access 9, 45668 – 45682.

Hochstetter, J.,
Vairetti, C.,
Cares, C.,
García, M.,
Maldonado, S.

Paper

Time-weighted Fuzzy Support Vector Machines for classification in changing environments.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – Information Sciences 559, 97-110.

Maldonado, S.,
López, J.,
Vairetti, C.

Paper

Efficient n-gram construction for text categorization using feature selection techniques.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – Intelligent Data Analysis 25, 509–525.

García, M.,
Maldonado, S.
Vairetti, C.

Publicaciones 2020

Paper

Profit-based Churn Prediction based on Minimax Probability Machines.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – European Journal of Operational Research 284(1), 273-284.

Maldonado, S.,
López, J.,
C. Vairetti.

Paper

Enhancing the classification of Social Media Opinions by Optimizing the Structural Information.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – Future Generation Computer Systems 102, 838-846.

C. Vairetti,
E. Martínez,
S. Maldonado,
F. Herrera,
M.V. Luzón.

Publicaciones 2019

Paper

Facilitating Conditions for Successful Adoption of Inter-Organizational Information Systems in Seaports.

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades.(…)

Fuente: 2021 – Transportation Research Part A: Policy and Practice 130, 333-350.

Vairetti, C.,
R. González-Ramírez,
S. Maldonado,
C. Álvarez,
S. Voss.