Abstract
Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicting a given example’s class by locating it in the “example space” and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning algorithm selection is compared to other methods for selecting from multiple learning algorithms. The approach is then extended to weight rather than select the algorithms according to their past performance in a given region. Both approaches are further evaluated on a set of ten domains and compared to several other meta-learning strategies.
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© 1996 Springer-Verlag New York, Inc.
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Merz, C.J. (1996). Dynamical Selection of Learning Algorithms. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_27
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DOI: https://doi.org/10.1007/978-1-4612-2404-4_27
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
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