# Deep Forest with Hashing Screening and Window Screening

arXiv: https://arxiv.org/abs/2207.11951 · HTML: https://mlsanity.com/paper/2207.11951/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments](https://mlsanity.com/paper/1705.10874/) (2017)
- [Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT](https://mlsanity.com/paper/2005.03264/) (2020)
- [Multi-Label Learning with Deep Forest](https://mlsanity.com/paper/1911.06557/) (2019)
- [Analyzing the tree-layer structure of Deep Forests](https://mlsanity.com/paper/2010.15690/) (2020)
- [DBC-Forest: Deep forest with binning confidence screening](https://mlsanity.com/paper/2112.13182/) (2021)

## Built on by (most cited first, 3 of 3)

- [Uncertainty Aware Neural Network from Similarity and Sensitivity](https://mlsanity.com/paper/2304.14925/) (2023)
- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)
- [Sequential three-way decisions with a single hidden layer feedforward neural network](https://mlsanity.com/paper/2303.07589/) (2023)

## Abstract (arXiv)

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced.