Sampling unknown decision functions to build classifier copies

Irene Unceta, Diego Palacios, Jordi Nin, Oriol Pujol

Research output: Book chapterConference contributionpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
EditorsVicenc Torra, Yasuo Narukawa, Jordi Nin, Núria Agell
PublisherSpringer
Pages192-204
Number of pages13
ISBN (Print)9783030575236
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 - Sant Cugat del Vallès, Spain
Duration: 2 Sept 20204 Sept 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12256 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020
Country/TerritorySpain
CitySant Cugat del Vallès
Period2/09/204/09/20

Keywords

  • Bayesian sampling
  • Classification
  • Copies
  • Synthetic data

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