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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2017

27-07-2016 | Original Article

Predicting run time of classification algorithms using meta-learning

Authors: Tri Doan, Jugal Kalita

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2017

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Abstract

Selecting a right classification algorithm is an important step for the success of any data mining project. Run time can be used to assess efficiency of a classification algorithm of interest. Experimenting with several algorithms can increase the cost of a data mining project. Using the idea of meta-learning, we present an approach to estimate the run time of a particular classification algorithm on an arbitrary dataset. Our work provides a way to evaluate the choice of an algorithm without experimental execution. We demonstrate that the use of multivariate adaptive regression splines significantly outperforms other regression models, even a ‘state-of-the-art’ algorithm such as support vector regression.
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Appendix
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Metadata
Title
Predicting run time of classification algorithms using meta-learning
Authors
Tri Doan
Jugal Kalita
Publication date
27-07-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0571-6

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