@inproceedings{df237ff8c94b44b2aa4921e7cc78815e,
title = "Sampling unknown decision functions to build classifier copies",
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.",
keywords = "Bayesian sampling, Classification, Copies, Synthetic data",
author = "{Unceta Mendieta}, Irene and Diego Palacios and J. Nin and Oriol Pujol",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 ; Conference date: 02-09-2020 Through 04-09-2020",
year = "2020",
doi = "10.1007/978-3-030-57524-3_16",
language = "English",
isbn = "9783030575236",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "192--204",
editor = "Vicenc Torra and Yasuo Narukawa and Jordi Nin and N{\'u}ria Agell",
booktitle = "Modeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings",
address = "United States",
}