sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)
sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)
Tong Liu, Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Journal Track. Pages 5752-5756.
https://doi.org/10.24963/ijcai.2022/804
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.
Keywords:
Machine Learning: Optimisation
Constraint Satisfaction and Optimization: Constraints and Machine Learning
Machine Learning: Applications
Machine Learning: Classification
Machine Learning: General