@inproceedings{3d263ab59815494eb3e6b8d757cc3573,
title = "Feature selection for SUNNY: A study on the algorithm selection library",
abstract = "Given a collection of algorithms, the Algorithm Selection (AS) problem consists in identifying which of them is the best one for solving a given problem. The selection depends on a set of numerical features that characterize the problem to solve. In this paper we show the impact of feature selection techniques on the performance of the SUNNY algorithm selector, taking as reference the benchmarks of the AS library (ASlib). Results indicate that a handful of features is enough to reach similar, if not better, performance of the original SUNNY approach that uses all the available features. We also present sunny-as: a tool for using SUNNY on a generic ASlib scenario.",
keywords = "Algorithm Portfolio, Algorithm Selection, Feature Selection, Machine Learning",
author = "Roberto Amadini and Fabio Biselli and Maurizio Gabbrielli and Tong Liu and Jacopo Mauro",
year = "2015",
month = dec,
day = "1",
doi = "10.1109/ICTAI.2015.18",
language = "English",
series = "Proceedings of the International Conference on Tools with Artificial Intelligence, ICTAI",
publisher = "IEEE Computer Society",
pages = "25--32",
booktitle = "Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015",
note = "27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015 ; Conference date: 09-11-2015 Through 11-11-2015",
}