On prediction mechanisms in fast branch & bound algorithms

Abstract

The idea of using the Branch & Bound search for optimal feature selection has been recently refined by introducing additional predicting heuristics that is able to considerably accelerate the search process while keeping the optimality of results unaffected. The heuristics is used most extensively in the so-called Fast Branch & Bound algorithm, where it replaces many slow criterion function computations by means of fast predictions. In this paper we investigate alternative prediction mechanisms. The alternatives are shown potentially useful for simplification and speed-up of the algorithm. We demonstrate the robustness of the prediction mechanism concept on real data experiments. © Springer-Verlag 2004.