A classification tree is grown by repeated partitioning of the dataset based on a predefined split criterion. The node split in the growth process depends only on the class ratio of the data chunk that gets split in every internal node of the tree. In a classification tree learning task, when the class ratio of the unlabeled part of the dataset is available, it becomes feasible to use the unlabeled data alongside the labeled data to train the tree in a semi-supervised style. Our motivation is to facilitate the usage of the abundantly available unlabeled data for building classification trees, as it is laborious and expensive to acquire labels. In this paper, we propose a semi-supervised approach to growing classification trees, where we adapted the Maximum Mean Discrepancy (MMD) method for estimating the class ratio at every node split. In our experimentation using several binary and multiclass classification datasets, we observed that our semi-supervised approach to growing a classification tree is statistically better than traditional decision tree algorithms in 31 of 40 datasets.

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