Publikation
Prediction of Classifier Training Time Including Parameter Optimization
Matthias Reif; Faisal Shafait; Andreas Dengel
In: Joscha Bach; Stefan Edelkamp (Hrsg.). Proceedings of the 34th Annual German Conference on Artificial Intelligence. German Conference on Artificial Intelligence (KI-11), 34th, October 4-7, Berlin, Germany, Pages 260-271, Lecture Notes in Computer Science (LNCS), Vol. 7006, ISBN 978-3-642-24454-4, Springer, Berlin, Heidelberg, 10/2011.
Zusammenfassung
Besides the classification performance, the training time is a second important
factor that affects the suitability of a classification algorithm regarding an
unknown dataset. An algorithm with a slightly lower accuracy is maybe preferred
if its training time is significantly lower. Additionally, an estimation of the
required training time of a pattern recognition task is very useful if the
result has to be available in a certain amount of time.
Meta-learning is often used to predict the suitability or performance of
classifiers using different learning schemes and features. Especially
landmarking features have been used very successfully in the past. The accuracy
of simple learners are used to predict the performance of a more sophisticated
algorithm.
In this work, we investigate the quantitative prediction of the training time
for several target classifiers. Different sets of meta-features are evaluated
according to their suitability of predicting actual run-times of a parameter
optimization by a grid search. Additionally, we adapted the concept of
landmarking to time prediction. Instead of their accuracy, the run-time of
simple learners are used as feature values.
We evaluated the approach on real world datasets from the UCI machine learning
repository and StatLib. The run-time of five different classification algorithms
are predicted and evaluated using two different performance measures. The
promising results show that the approach is able to reasonably predict the
training time including a parameter optimization. Furthermore, different sets of
meta-features seem to be necessary for different target algorithms in order to
achieve the highest prediction performances.