  A new boosting decision tree (BDT) method, QBDT, is proposed for the
classification problem in the field of high energy physics (HEP). In many HEP
researches, great efforts are made to increase the signal significance with the
presence of huge background and various systematical uncertainties. Why not
develop a BDT method targeting the significance directly? Indeed, the
significance plays a central role in this new method. It is used to split a
node in building a tree and to be also the weight contributing to the BDT
score. As the systematical uncertainties can be easily included in the
significance calculation, this method is able to learn about reducing the
effect of the systematical uncertainties via training. Taking the search of the
rare radiative Higgs decay in proton-proton collisions $pp \to h + X \to
\gamma\tau^+\tau^-+X$ as example, QBDT and the popular Gradient BDT (GradBDT)
method are compared. QBDT is found to reduce the correlation between the signal
strength and systematical uncertainty sources and thus to give a better
significance. The contribution to the signal strength uncertainty from the
systematical uncertainty sources using the new method is 50-85~\% of that using
the GradBDT method.
