DBC-Forest: Deep forest with binning confidence screening
This is an incremental improvement for deep forest models, addressing efficiency and accuracy issues in machine learning applications.
The paper tackles the problem of mis-partitioned instances in deep confidence screening forests by proposing DBC-Forest, which bins instances based on confidence to improve accuracy and speed, achieving highly accurate predictions and faster performance than similar models.
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.