Tree Kernel Usage in Naive Bayes Classifiers

Abstract

We present a novel approach in machine learning by combining naive Bayes classifiers with tree kernels. Tree kernel methods produce promising results in machine learning tasks containing treestructured attribute values. These kernel methods are used to compare two tree-structured attribute values recursively. Up to now tree kernels are only used in kernel machines like Support Vector Machines or Perceptrons. In this paper, we show that tree kernels can be utilized in a naive Bayes classifier enabling the classifier to handle tree-structured values. We evaluate our approach on three datasets containing tree-structured values. We show that our approach using tree-structures delivers significantly better results in contrast to approaches using non-structured (flat) features extracted from the tree. Additionally, we show that our approach is significantly faster than comparable kernel machines in several settings which makes it more useful in resource-aware settings like mobile devices.