  We study the problem of sharing as many branching conditions of a given
forest classifier or regressor as possible while keeping classification
performance. As a constraint for preventing from accuracy degradation, we first
consider the one that the decision paths of all the given feature vectors must
not change. For a branching condition that a value of a certain feature is at
most a given threshold, the set of values satisfying such constraint can be
represented as an interval. Thus, the problem is reduced to the problem of
finding the minimum set intersecting all the constraint-satisfying intervals
for each set of branching conditions on the same feature. We propose an
algorithm for the original problem using an algorithm solving this problem
efficiently. The constraint is relaxed later to promote further sharing of
branching conditions by allowing decision path change of a certain ratio of the
given feature vectors or allowing a certain number of non-intersected
constraint-satisfying intervals. We also extended our algorithm for both the
relaxations. The effectiveness of our method is demonstrated through
comprehensive experiments using 21 datasets (13 classification and 8 regression
datasets in UCI machine learning repository) and 4 classifiers/regressors
(random forest, extremely randomized trees, AdaBoost and gradient boosting).
