  As a deep learning model, deep confidence screening forest (gcForestcs) has
achieved great success in various applications. Compared with the traditional
deep forest approach, gcForestcs effectively reduces the high time cost by
passing some instances in the high-confidence region directly to the final
stage. However, there is a group of instances with low accuracy in the
high-confidence region, which are called mis-partitioned instances. To find
these mis-partitioned instances, this paper proposes a deep binning confidence
screening forest (DBC-Forest) model, which packs all instances into bins based
on their confidences. In this way, more accurate instances can be passed to the
final stage, and the performance is improved. Experimental results show that
DBC-Forest achieves highly accurate predictions for the same hyperparameters
and is faster than other similar models to achieve the same accuracy.
