sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)

Tong Liu; Roberto Amadini; Maurizio Gabbrielli; Jacopo Mauro

IJCAI 2022poster0 citations

sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)

Tong Liu, Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro

Abstract

SUNNY is a k-nearest neighbors based Algorithm Selection (AS) approach that schedules and runs a number of solvers for a given unforeseen problem. In this work we present sunny-as2, an enhancement of SUNNY for generic AS scenarios that advances the original approach with wrapper-based feature selection, neighborhood-size configuration and a greedy approach to speed-up the training phase. Empirical evidence shows that sunny-as2 is competitive w.r.t. state-of-the-art AS approaches.

Machine Learning: OptimisationConstraint Satisfaction and Optimization: Constraints and Machine LearningMachine Learning: ApplicationsMachine Learning: ClassificationMachine Learning: General

BibTeX

@inproceedings{ijcai2022p804,
  title     = {sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)},
  author    = {Liu, Tong and Amadini, Roberto and Gabbrielli, Maurizio and Mauro, Jacopo},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {5752--5756},
  year      = {2022},
  month     = {7},
  note      = {Journal Track},
  doi       = {10.24963/ijcai.2022/804},
  url       = {https://doi.org/10.24963/ijcai.2022/804},
}