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Robust optimal classification trees under noisy labels

Autor: Víctor Blanco, Justo Puerto, Alberto Japón

Publikováno v: Digibug. Repositorio Institucional de la Universidad de Granada
instname

This research has been partially supported by Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion/FEDER grant number PID2020-114594GBC21, Junta de Andalucia projects P18-FR-1422, P18-FR-2369 and projects FEDERUS-1256951, BFQM

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61fe83b7587a1f9f4649137daca94a32
http://hdl.handle.net/10481/71316

Sparsity in Optimal Randomized Classification Trees

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

Publikováno v: European Journal of Operational Research
idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
idUS. Depósito de Investigación de la Universidad de Sevilla
instname

Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding a very fast training process; however, controlling sparsity (a proxy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2a851405cd333b96184e8e96ecd04b4
http://arxiv.org/abs/2002.09191

From optimization-based machine learning to interpretable security rules for operation

Autor: Goran Strbac, Ioannis Konstantelos, Jochen L. Cremer

Various supervised machine learning approaches have been used in the past to assess the power system security (also known as reliability). This is typically done by training a classifier on a large number of operating points whose postfault status (s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a663942b2fd042fd2d98a445df7a157
http://hdl.handle.net/10044/1/68788