Published November 9, 2015 | Version v1
Conference paper Metadata-only

Feature Selection for SUNNY: a Study on the Algorithm Selection Library

Amadini, Roberto
Biselli, Fabio
Gabbrielli, Maurizio
Liu, Tong
Mauro, Jacopo
Others:
Department of Computer Science and Engineering [Bologna] (DISI) ; Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO)
Foundations of Component-based Ubiquitous Systems (FOCUS) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Dipartimento di Informatica - Scienza e Ingegneria [Bologna] (DISI) ; Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO)-Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO)

Description

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.

Abstract

International audience

Additional details

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Created:
March 25, 2023
Modified:
November 30, 2023