@inproceedings{3bb39198333e4c9694638371c4eb27c4,
title = "Towards Proximity Graph Auto-configuration - An Approach Based on Meta-learning",
abstract = "Due to the high production of complex data, the last decades have provided a huge advance in the development of similarity search methods. Recently graph-based methods have outperformed other ones in the literature of approximate similarity search. However, a graph employed on a dataset may present different behaviors depending on its parameters. Therefore, finding a suitable graph configuration is a time-consuming task, due to the necessity to build a structure for each parameterization. Our main contribution is to save time avoiding this exhaustive process. We propose in this work an intelligent approach based on meta-learning techniques to recommend a suitable graph along with its set of parameters for a given dataset. We also present and evaluate generic and tuned instantiations of the approach using Random Forests as the meta-model. The experiments reveal that our approach is able to perform high quality recommendations based on the user preferences.",
keywords = "Auto configuration, Meta-learning, Nearest neighbor search, Proximity graphs",
author = "Oyamada, {Rafael Seidi} and Shimomura, {Larissa Capobianco} and Junior, {Sylvio Barbon} and Kaster, {Daniel S.}",
year = "2020",
doi = "10.1007/978-3-030-54832-2_9",
language = "English",
isbn = "978-3-030-54831-5",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "93--107",
editor = "J{\'e}r{\^o}me Darmont and Boris Novikov and Robert Wrembel",
booktitle = "Advances in Databases and Information Systems - 24th European Conference, ADBIS 2020, Proceedings",
address = "Germany",
note = "24th European Conference on Advances in Databases and Information Systems - ADBIS 2020, ADBIS ; Conference date: 25-08-2020 Through 27-01-2021",
}