Cross-Disciplinary Perspectives on Meta-Learning for Algorithm Selection KATE A. SMITH-MILES Monash University, Australia 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 eld of meta-learning, focused on learning about learning algorithm performance on classi cation problems. But there has been only limited generalization of these ideas beyond classi cation, 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 uni ed 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
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Cross-disciplinary perspectives on meta-learning for algorithm selection
Smith-Miles, Kate A.
Follow ACM Computing Surveys (CSUR)
, Volume 41 (1)
Association for Computing Machinery – Dec 1, 2008
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