Towards Proximity Graph Auto-configuration - An Approach Based on Meta-learning.

Rafael Seidi Oyamada, Larissa Capobianco Shimomura, Sylvio Barbon Junior, Daniel S. Kaster

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Databases and Information Systems - 24th European Conference, ADBIS 2020, Proceedings
EditorsJérôme Darmont, Boris Novikov, Robert Wrembel
Place of PublicationCham
PublisherSpringer
Pages93-107
Number of pages15
ISBN (Print)978-3-030-54831-5
DOIs
Publication statusPublished - 2020
Event24th European Conference on Advances in Databases and Information Systems - ADBIS 2020 -
Duration: 25 Aug 202027 Jan 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12245 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th European Conference on Advances in Databases and Information Systems - ADBIS 2020
Abbreviated titleADBIS
Period25/08/2027/01/21

Keywords

  • Auto configuration
  • Meta-learning
  • Nearest neighbor search
  • Proximity graphs

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