Improving Deep Forest by Confidence Screening

Pang, Muye; Ting, Kai Ming; Zhao, Peng; Zhou, Zhi‐Hua

doi:10.1109/icdm.2018.00158

Cited by 44 publications

(26 citation statements)

References 15 publications

(19 reference statements)

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“…MDForest consists of two parts: multi-dimensional and multi-grained scanning and dense cascade forest. 32,33 Multi-dimensional and multi-grained scanning realizes the re-representation process of features. The multi-scale features are captured by a multi-grained scanning mechanism to enhance the representation learning ability of the cascade forest.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

Shao

Zou

2021

J. Appl. Rem. Sens.

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Cloud detection in satellite images is a vital step for cloud/land recognition, cloud/ snow discrimination, and cloud shadow removal. Accurate cloud detection plays an important role in land resource management, environmental pollution monitoring, and land target recognition. Deep learning (DL) algorithms have shown great progress in cloud detection. However, as the complexity of the DL-based model increases, cloud detection efficiency decreases. DLbased cloud detection models are unable to successfully balance the performance-efficiency tradeoff. In our study, a multi-dimensional and multi-grained dense cascade forest (MDForest) is proposed for multi-spectral cloud detection. MDForest is a deep forest structure that automatically extracts low-level and high-level features from satellite cloud images end-to-end; a multi-dimensional and multi-grained scanning mechanism is introduced to capture the spectral information of multi-spectral satellite images while enhancing the representation learning ability of cascade forest. The experimental results on the HJ-1A/1B dataset show that MDForest improves the performance of cloud detection and possesses a good inference efficiency compared with DL-based cloud detection methods, which makes the proposed MDForest satisfy the application where good performance and high efficiency are both required. © The Authors.Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Proposed Methodsmentioning

confidence: 99%

Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

Shao

Zou

2021

J. Appl. Rem. Sens.

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“…Different gene expression information had strong correlation, which means that it is better not to discretize them when classifying cancer subtypes on gene expression data. This will lead to various classifiers (such as decision trees and random forests) showing poor performance in the classification of cancer subtypes [ 32 ]. FNT is a new form of neural network, which has many advantages that traditional deep neural network models do not have (for example, the structure and parameters of the model can be automatically optimized, feature selection can be performed automatically, etc.)…”

Section: Methodsmentioning

confidence: 99%

A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data

Zhong

Meng

Chen

2021

Computational Intelligence and Neuroscience

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The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model’s structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

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“…The resulting problem is that the feature vector after multigrained scanning is a dense feature vector, which reduces the efficiency of subsequent Cascade Forest training and inference, and increases memory consumption and running time. Based on the above problems, we introduce the operation of Pang [33] to randomly sample the features of multi-grained scanning to improve training efficiency.…”

Section: B Sub-sampling Multi-grained Scanningmentioning

confidence: 99%

“…In order to evaluate the performance of the RES-gcForest, compared with other existing technologies, including Cascade Forest, Random Forest (RF) [33], Neural Network (NN), Convolutional Neural Network (CNN), gcForest, and ResNet.…”

Section: Comparison Modelmentioning

confidence: 99%

Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery

et al. 2021

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Cloud/snow recognition is one application of satellite remote sensing imagery in natural disaster monitoring. Deep learning technology has contributed to the improvement of the performance of cloud/snow recognition. However, deep learning-based methods cannot well balance the performance and efficiency of cloud/snow recognition. In this paper, an augmented multi-dimensional and multi-grained Cascade Forest is proposed for cloud/snow recognition. The multi-dimensional deep forest structure with the representation learning ability allows it to capture the spatial and spectral information of cloud/snow satellite imagery accordingly equipped with good recognition efficiency. Besides, a simple augmentation Random Erasing method is introduced for enhancing the robustness of cloud/snow recognition. The experimental results on the HJ-1A/1B dataset show that the proposed method improves the performance of cloud/snow recognition by extracting spectral information from multi-spectral satellite imagery. In addition, based on the tree-based structure, the proposed method well balances the performance and efficiency of cloud/snow recognition, which can be considered as an alternative to the Neural Network for cloud/snow recognition.INDEX TERMS Cloud/snow recognition, multi-dimensional and multi-grained, Random Erasing, representation learning.

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Improving Deep Forest by Confidence Screening

Cited by 44 publications

References 15 publications

Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data

Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery

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