Name
Abstract | ||
|---|---|---|
Many algorithms for data analysis exist, especially for classification problems. To solve a data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper, we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order to do so, we reduced this problem to the multi-armed bandit problem. We consider an algorithm as an arm and algorithm hyperparameters search during a fixed time as the corresponding arm play. We also suggest a problem-specific reward function. We performed the experiments on 10 real datasets and compare the suggested method with the existing one implemented in Auto-WEKA. The results show that our method is significantly better in most of the cases and never worse than the Auto-WEKA. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-030-35400-8_2 | arXiv: Learning |
DocType | Volume | Citations |
Journal | abs/1611.02053 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valeria Efimova | 1 | 0 | 1.01 |
| Andrey Filchenkov | 2 | 46 | 15.80 |
| Anatoly Shalyto | 3 | 0 | 0.34 |