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.
Towards Proximity Graph Auto-configuration: An Approach Based on Meta-learning / R.S. Oyamada, L.C. Shimomura, S.B. Junior, D.S. Kaster (LECTURE NOTES IN COMPUTER SCIENCE). - In: Advances in Databases and Information Systems / [a cura di] J. Darmont, B. Novikov, R. Wrembel. - [s.l] : Springer, 2020. - ISBN 978-3-030-54831-5. - pp. 93-107 (( Intervento presentato al 24. convegno European Conference, ADBIS tenutosi a Lyon nel 2020 [10.1007/978-3-030-54832-2_9].
Towards Proximity Graph Auto-configuration: An Approach Based on Meta-learning
R.S. Oyamada
;
2020
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.Pubblicazioni consigliate
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