Abstract/Summary

Forests of decision trees are a popular tool for classification applications. This paper presents an approach to evolving the forest classifier, reducing the time spent designing the optimal tree depth and forest size. This is applied to the task of vehicle classification for purposes of verification against databases at security checkpoints, or accumulation of road usage statistics. The evolutionary approach to building the forest classifier is shown to out-perform a more typically grown forest and a baseline neural-network classifier for the vehicle classification task.