Sunny-As2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)
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.
Cite
Text
Liu et al. "Sunny-As2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/804Markdown
[Liu et al. "Sunny-As2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/liu2022ijcai-sunny/) doi:10.24963/IJCAI.2022/804BibTeX
@inproceedings{liu2022ijcai-sunny,
title = {{Sunny-As2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)}},
author = {Liu, Tong and Amadini, Roberto and Gabbrielli, Maurizio and Mauro, Jacopo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5752-5756},
doi = {10.24963/IJCAI.2022/804},
url = {https://mlanthology.org/ijcai/2022/liu2022ijcai-sunny/}
}