  This paper considers semi-supervised learning for tabular data. It is widely
known that Xgboost based on tree model works well on the heterogeneous features
while transductive support vector machine can exploit the low density
separation assumption. However, little work has been done to combine them
together for the end-to-end semi-supervised learning. In this paper, we find
these two methods have complementary properties and larger diversity, which
motivates us to propose a new semi-supervised learning method that is able to
adaptively combine the strengths of Xgboost and transductive support vector
machine. Instead of the majority vote rule, an optimization problem in terms of
ensemble weight is established, which helps to obtain more accurate pseudo
labels for unlabeled data. The experimental results on the UCI data sets and
real commercial data set demonstrate the superior classification performance of
our method over the five state-of-the-art algorithms improving test accuracy by
about $3\%-4\%$. The partial code can be found at
https://github.com/hav-cam-mit/CTO.
