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Tesis Doctoral
New models and methods for classification and feature selection. a mathematical optimization perspective
(2021-07-27)
The objective of this PhD dissertation is the development of new models for Supervised Classification and Benchmarking, making use of Mathematical Optimization and Statistical tools. Particularly, we address the fusion ...
Tesis Doctoral
Computational Methods for the Analysis of Complex Data
(2021-07-07)
This PhD dissertation bridges the disciplines of Operations Research and Statistics to develop novel computational methods for the extraction of knowledge from complex data. In this research, complex data stands for ...
Artículo
Cost-sensitive feature selection for support vector machines
(Elsevier, 2018-03)
Feature Selection (FS) is a crucial procedure in Data Science tasks such as Classification, since it identifies the relevant variables, making thus the classification procedures more interpretable and more effective by ...
Artículo
Constrained Naïve Bayes with application to unbalanced data classification
(Springer, 2021-09-15)
The Naïve Bayes is a tractable and efficient approach for statistical classification. In general classification problems, the consequences of misclassifications may be rather different in different classes, making it ...
Artículo
Variable selection for Naïve Bayes classification
(Pergamon-Elsevier Science Ltd., 2021-07-06)
The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional ...
Artículo
Variable selection for Naïve Bayes classification
(Elsevier, 2021)
The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional i ...
Artículo
A cost-sensitive constrained Lasso
(Springer, 2020-03-02)
The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full ...
Artículo
On support vector machines under a multiple-cost scenario
(Springer, 2018-07-31)
Support vector machine (SVM) is a powerful tool in binary classification, known to attain excellent misclassification rates. On the other hand, many realworld classification problems, such as those found in medical ...