Predicting run time of classification algorithms using meta-learning

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.