Constrained Naïve Bayes with application to unbalanced data classification

Autor: Blanquero Bravo, Rafael, Carrizosa Priego, Emilio José, Ramírez Cobo, Josefa, Sillero Denamiel, María Remedios

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 crucial to control misclassifica

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3272::e8e0545171230e35bcf0a1b20600e32c

On sparse ensemble methods: an application to short-term predictions of the evolution of COVID-19

Autor: Benítez Peña, Sandra, Carrizosa Priego, Emilio José, Guerrero, Vanesa, Jiménez Gamero, María Dolores

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3272::f5b8e2ac15f9996f3e0c1bc51f4b8d96

Interpreting clusters via prototype optimization

Autor: Carrizosa Priego, Emilio José, Kurishchenko, Kseniia, Marín, Alfredo, Romero Morales, María Dolores

In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analy sis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely and distinctively as possible, i.e., th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3272::8fe4d010fc3f5028477fe303ab371c0a

Integer constraints for enhancing interpretability in linear regression

Autor: Carrizosa Priego, Emilio José, Olivares Nadal, Alba Victoria, Ramírez Cobo, Josefa

Publikováno v: idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
idUS. Depósito de Investigación de la Universidad de Sevilla
instname

One of the main challenges researchers face is to identify the most relevant features in a prediction model. As a consequence, many regularized methods seeking sparsity have flourished. Although sparse, their solutions may not be interpretable in the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::84c3a7c87d2feab2aff414efa6273c2b

Cost-sensitive feature selection for support vector machines

Autor: Benítez Peña, Sandra, Blanquero Bravo, Rafael, Carrizosa Priego, Emilio José, Ramírez Cobo, Josefa

Publikováno v: idUS. Depósito de Investigación de la Universidad de Sevilla
instname

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 reducing noise and data overfit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=RECOLECTA___::a901768ca943b981ccdbaa95f38b4646