Sampling unknown decision functions to build classifier copies

Irene Unceta, Diego Palacios, Jordi Nin, Oriol Pujol

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

4 Cites (Scopus)

Resum

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 originalAnglès
Títol de la publicacióModeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
EditorsVicenc Torra, Yasuo Narukawa, Jordi Nin, Núria Agell
EditorSpringer
Pàgines192-204
Nombre de pàgines13
ISBN (imprès)9783030575236
DOIs
Estat de la publicacióPublicada - 2020
Publicat externament
Esdeveniment17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 - Sant Cugat del Vallès, Spain
Durada: 2 de set. 20204 de set. 2020

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum12256 LNAI
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

Conferència

Conferència17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020
País/TerritoriSpain
CiutatSant Cugat del Vallès
Període2/09/204/09/20

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