Numerous problems require an on-line treatment. The variation of the problem instance makes it harder to solve: an algorithm used may be very efficient for a long period but suddenly its performance deteriorates due to a change in the environment. It could be judicious to switch to another algorithm in order to adapt to the environment changes. In this paper, we focus on the prediction on-the-fly. We have several on-line prediction algorithms at our disposal, each of them may have a different behaviour than the others depending on the situation. First, we address a meta-algorithm named SEA developed for experts algorithms. Next, we propose a modified version of it to improve its performance in the context of the on-line prediction. We confirm the efficiency gain we obtained through this modification in experimental manner.
CITATION STYLE
Dambreville, A., Tomasik, J., & Cohen, J. (2016). Meta-algorithm to choose a good on-line prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10083 LNCS, pp. 126–130). Springer Verlag. https://doi.org/10.1007/978-3-319-49259-9_10
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