Using genetic algorithms to improve prediction of execution times of ML tasks

Abstract

Experimental procedures associated with Machine Learning (ML) techniques are usually computationally demanding. An important step for a conscientious allocation of ML tasks into resources is predicting their execution times. Previously, empirical comparisons using a Meta-learning framework indicated that Support Vector Machines (SVM) are suited for this problem; however, their performance is affected by the choice of parameter values and input features. In this paper, we tackle the issue by applying Genetic Algorithm (GA) to perform joint Feature Subset Selection (FSS) and Parameters Optimization (PO). At first, a GA is used for FSS+PO in SVMs with two kernel functions, independently. Later, besides FSS+PO an additional term is evolved to weight predictions of both models to build a combined regressor. An empirical investigation conducted for predicting execution times of 6 ML algorithms over 78 publicly available datasets unveils a higher accuracy when compared with the previous results. © 2012 Springer-Verlag.