As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Many state of the art Algorithm Selection systems use Machine Learning to either predict the run time or a similar performance measure of each of a set of algorithms and choose the algorithm with the best predicted performance or predict the best algorithm directly. We present a technique based on the well-established Machine Learning technique of stacking that combines the two approaches into a new hybrid approach and predicts the best algorithm based on predicted run times. We demonstrate significant performance improvements of up to a factor of six compared to the previous state of the art. Our approach is widely applicable and does not place any restrictions on the performance measure used, the way to predict it or the Machine Learning used to predict the best algorithm. We investigate different ways of deriving new Machine Learning features from the predicted performance measures and evaluate their effectiveness in increasing performance further. We use five different regression algorithms for performance prediction on five data sets from the literature and present strong empirical evidence that shows the effectiveness of our approach.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.