  In recent years, dynamically growing data and incrementally growing number of
classes pose new challenges to large-scale data classification research. Most
traditional methods struggle to balance the precision and computational burden
when data and its number of classes increased. However, some methods are with
weak precision, and the others are time-consuming. In this paper, we propose an
incremental learning method, namely, heterogeneous incremental Nearest Class
Mean Random Forest (hi-RF), to handle this issue. It is a heterogeneous method
that either replaces trees or updates trees leaves in the random forest
adaptively, to reduce the computational time in comparable performance, when
data of new classes arrive. Specifically, to keep the accuracy, one proportion
of trees are replaced by new NCM decision trees; to reduce the computational
load, the rest trees are updated their leaves probabilities only. Most of all,
out-of-bag estimation and out-of-bag boosting are proposed to balance the
accuracy and the computational efficiency. Fair experiments were conducted and
demonstrated its comparable precision with much less computational time.
