Predicting run time of classification algorithms using meta-learning

Tri Doan

doi:10.1007/s13042-016-0571-6

2016-07-27

PRE

Tri Doan *

Jugal Kalita

DOI：10.1007/s13042-016-0571-6

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摘要

En 中文

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.

Keyword:

Classification

Meta-learning

Regression model

Runtime

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期刊

International Journal of Machine Learning and Cybernetics

IF:

2.7

论文数:

3.2K

被引数:

5.6K

机构

University of Colorado System

学者数:

6.3W

论文数: 5.5W

被引数: 1.8K

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