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sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)
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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.
International Joint Conferences on Artificial Intelligence Organization
Title: sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)
Description:
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.
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