# DBC-Forest: Deep forest with binning confidence screening

arXiv: https://arxiv.org/abs/2112.13182 · HTML: https://mlsanity.com/paper/2112.13182/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 25 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Multi-Label Learning with Deep Forest](https://mlsanity.com/paper/1911.06557/) (2019)
- [Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest](https://mlsanity.com/paper/1807.05711/) (2018)

## Built on by (most cited first, 2 of 2)

- [Deep Forest with Hashing Screening and Window Screening](https://mlsanity.com/paper/2207.11951/) (2022)
- [Sequential three-way decisions with a single hidden layer feedforward neural network](https://mlsanity.com/paper/2303.07589/) (2023)

## Abstract (arXiv)

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.