research-article

Cross-disciplinary perspectives on meta-learning for algorithm selection

Author:

Kate A. Smith-MilesAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 41, Issue 1

Article No.: 6, Pages 1 - 25

https://doi.org/10.1145/1456650.1456656

Published: 15 January 2009 Publication History

Abstract

The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm is likely to perform best for my problem? Recognizing the problem as a learning task in the early 1990's, the machine learning community has developed the field of meta-learning, focused on learning about learning algorithm performance on classification problems. But there has been only limited generalization of these ideas beyond classification, and many related attempts have been made in other disciplines (such as AI and operations research) to tackle the algorithm selection problem in different ways, introducing different terminology, and overlooking the similarities of approaches. In this sense, there is much to be gained from a greater awareness of developments in meta-learning, and how these ideas can be generalized to learn about the behaviors of other (nonlearning) algorithms. In this article we present a unified framework for considering the algorithm selection problem as a learning problem, and use this framework to tie together the crossdisciplinary developments in tackling the algorithm selection problem. We discuss the generalization of meta-learning concepts to algorithms focused on tasks including sorting, forecasting, constraint satisfaction, and optimization, and the extension of these ideas to bioinformatics, cryptography, and other fields.

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Index Terms

Cross-disciplinary perspectives on meta-learning for algorithm selection
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 41, Issue 1

January 2009

281 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1456650

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Publication History

Published: 15 January 2009

Accepted: 01 March 2008

Revised: 01 December 2007

Received: 01 July 2007

Published in CSUR Volume 41, Issue 1

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