Sampling unknown decision functions to build classifier copies

Irene Unceta, Diego Palacios, Jordi Nin, Oriol Pujol

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs. A fundamental step of the copying process is generating an unlabelled set of points to explore the decision behavior of the targeted classifier throughout the input space. In this article we propose two sampling strategies to produce such sets. We validate them in six well-known problems and compare them with two standard methods in terms of both their accuracy performance and their computational cost.

Idioma originalInglés
Título de la publicación alojadaModeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
EditoresVicenc Torra, Yasuo Narukawa, Jordi Nin, Núria Agell
EditorialSpringer
Páginas192-204
Número de páginas13
ISBN (versión impresa)9783030575236
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 - Sant Cugat del Vallès, Espana
Duración: 2 sept 20204 sept 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12256 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020
País/TerritorioEspana
CiudadSant Cugat del Vallès
Período2/09/204/09/20

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