Meta-algorithm to Choose a Good On-Line Prediction

Alexandre Dambreville (1) , Joanna Tomasik (2) , Johanne Cohen (2)

1 CentraleSupélec
2 LRI - Laboratoire de Recherche en Informatique

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.

Domaines: Algorithme et structure de données [cs.DS]

Alexandre Dambreville, Joanna Tomasik, Johanne Cohen. Meta-algorithm to Choose a Good On-Line Prediction. Stabilization, Safety, and Security of Distributed Systems International Symposium (SSS), Nov 2016, Lyon, France. ⟨hal-01566270⟩