A fast fuzzy decision tree for color filtering

Fuzzy decision trees have been gaining popularity in the past two decades. They are the fuzzy extensions of crisp decision trees, introducing fuzzy logic into the nodes of the tree, thus making their generalization capabilities more robust. This paper presents a fuzzy decision tree architecture that is optimized for quick inference, in order to make the classification process as fast as possible. Furthermore, two training algorithms are presented to incrementally train fuzzy decision trees for realtime classification applications.