On Mathematical Optimization for Clustering Categories in Contingency Tables

Autor: Vanesa Guerrero, Dolores Romero Morales, Emilio Carrizosa

Publikováno v: e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
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Many applications in data analysis study whether two categorical variables are independent using a function of the entries of their contingency table. Often, the categories of the variables, associated with the rows and columns of the table, are grou

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83dc665b3f516ec0bffe21f124025bff
https://doi.org/10.1007/s11634-022-00508-4

On Optimal Regression Trees to Detect Critical Intervals for Multivariate Functional Data

Autor: Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales

Publikováno v: Computers & Operations Research

In this paper, we tailor optimal randomized regression trees to handle multivariate functional data. A compromise between prediction accuracy and sparsity is sought. Whilst fitting the tree model, the detection of a reduced number of intervals that a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c4a4fb7b057fbbc19f63733d93adb0f
https://research.cbs.dk/en/publications/40e936a4-9f62-4ac1-859b-94508ce1eb01

On sparse optimal regression trees

Autor: Cristina Molero-Río, Dolores Romero Morales, Rafael Blanquero, Emilio Carrizosa

Publikováno v: European Journal of Operational Research
idUS. Depósito de Investigación de la Universidad de Sevilla
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In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desira

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ffb2da518ce412074ddd8d926b4999d0

Optimal randomized classification trees

Autor: Dolores Romero Morales, Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río

Publikováno v: Computers & Operations Research

Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and the associ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcac8aa2d9b3b64d47e24314c7141a79

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

Autor: Emilio Carrizosa, M. Remedios Sillero-Denamiel, Belen Martin-Barragan, Pepa Ramírez-Cobo, Dolores Romero Morales, Sandra Benítez-Peña, M. Dolores Jiménez-Gamero, Vanesa Guerrero, Cristina Molero-Río

Publikováno v: European Journal of Operational Research 295 (2021) 648–663
RODIN. Repositorio de Objetos de Docencia e Investigación de la Universidad de Cádiz
instname
European Journal of Operational Research
Benítez-Peña, S, Carrizosa, E, Guerrero, V, Jiménez-Gamero, M D, Martín-Barragán, B, Molero-Río, C, Ramírez-Cobo, P, Romero Morales, D & Sillero-Denamiel, M R 2021, ' On sparse ensemble methods : An application to short-term predictions of the evolution of COVID-19 ', European Journal of Operational Research, vol. 295, no. 2, pp. 648-663 . https://doi.org/10.1016/j.ejor.2021.04.016

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=doi_dedup___::17f11ba09983204bc40990b0375e0d7d
http://hdl.handle.net/10498/25655