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Título
Isotonic boosting classification rules
Autor
Año del Documento
2020
Documento Fuente
Advances in Data Analysis and Classification. pag 1-25.
Resumo
In many real classi cation problems a monotone relation between some predictors and the classes may be assumed when higher (or lower) values of those predictors are related to higher levels of the response. In this paper, we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. These algorithms are based on theoretical developments that consider isotonic regression. We show the good performance of these procedures not only on simulations, but also on real data sets coming from two very different contexts, namely cancer diagnostic and failure of induction motors.
ISSN
1862-5347
Revisión por pares
SI
Version del Editor
Propietario de los Derechos
Spinger
Idioma
spa
Tipo de versión
info:eu-repo/semantics/acceptedVersion
Derechos
restrictedAccess
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