Person:
Ramos Del Olmo, Ángel Manuel

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First Name
Ángel Manuel
Last Name
Ramos Del Olmo
URI
https://hdl.handle.net/20.500.14352/75689
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Análisis Matemático Matemática Aplicada
Area
Matemática Aplicada
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Author: Carrasco, Miguel ×

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    Embedded Feature Selection for Robust Probability Learning Machines
    (2024) Carrasco, Miguel; Ivorra, Benjamín Pierre Paul; López, Julio; Ramos Del Olmo, Ángel Manuel
    Feature selection is essential for building effective machine learning models in binary classification. Eliminating unnecessary features can reduce the risk of overfitting and improve classification performance. Moreover, the data we handle always has a stochastic component, making it important to have robust models that are insensitive to data perturbations. Although there are numerous methods and tools for feature selection, relatively few works deal with embedded feature selection performed with robust classification models. In this work, we introduce robust classifiers with integrated feature selection capabilities, utilizing probability machines based on different penalization techniques such as the L1-norm or the elastic-net, combined with a novel Direct Feature Elimination process. Numerical experiments on standard databases demonstrate the effectiveness and robustness of the proposed models in classification tasks with a reduced number of features, using original indicators.The study also discusses the trade-offs in combining different penalties to select the most relevant features while minimizing empirical risk.
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    A Note on Probability and Conditional Probability SVM models
    (2024) Carrasco, Miguel; López, Julio; Ivorra, Benjamín Pierre Paul; Ramos Del Olmo, Ángel Manuel
    Support Vector Machines (SVMs) are powerful tools in machine learning, widely used for classification and regression tasks. Over time, various extensions, such as Probability Estimation SVM (PSVM) and Conditional Probability SVM (CPSVM), have been proposed to enhance SVM performance across different conditions and datasets. This article offers a comprehensive review and analysis of SVMs, with a particular focus on the PSVM and CPSVM models. We delve into the intricacies of these models, addressing computational nuances and presenting corrected formulations where necessary. Our empirical evaluation, conducted on diverse benchmark datasets, implements both linear and nonlinear versions of these SVMs. Performance is benchmarked using the Balanced Accuracy metric. The results highlight the comparative strengths of these models in handling varied datasets and their potential advantages over traditional SVM formulations. To rigorously assess and compare the performance of these SVM variants, we employ statistical tests, including the Friedman test and post hoc analysis.