Deep forest based multivariate classification for diagnostic health monitoring

Wang, Cunsong; Lu, Ningyun; Cheng, Yuehua; Jiang, Bin

doi:10.1109/ccdc.2018.8408224

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…They are spliced into a 12 dimensional probability vector. DF adopts the layer by layer structure of deep neural network (DNN) [19], and splices the enhancement vector output by previous layer and the original feature vector as the input of the next layer. In this way, we make a feature change on the basis of retaining the original data.…”

Section: Cascade Forestmentioning

confidence: 99%

Application of improved deep forest algorithm in image recognition

Shi

Liu²,

Fang³

et al. 2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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With the continuous development of artificial intelligence technology, the method based on deep learning has become a research hotspot in the field of engineering. However, the setting of super parameters and the amount of training datas are strictly required for the deep neural networks, which is difficult to meet the needs of rapid, accurate and stable diagnosis in practical industry. To solve above problems, a diagnosis method based on improved deep forest was proposed, which is applied to small-scale data sets. The cascade forest stage of traditional deep forest model has problems like high computational cost and redundant data processing, therefore, in the paper a deep forest model based on Linear Discriminant Analysis (LDA) feature extraction is designed. The improved deep forest model improves the data transmission and processing ability in multi-granularity scanning and cascade forest, and enhances the feature representation in the model while ensuring the data diversity, so as to improve the operation efficiency and diagnosis performance of the algorithm. Compared with the traditional machine learning methods, the experimental results show that the improved method has better accuracy.

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Section: Cascade Forestmentioning

confidence: 99%

Application of improved deep forest algorithm in image recognition

Shi

Liu²,

Fang³

et al. 2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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“…Then, the gcForest was deployed for modelling based on the pre-processed data. Wang et al (2018) [26] proposed a combined method for fault diagnosis. A feature selection approach based on Spearman's correlation was first deployed to remove redundant features.…”

Section: B the Studies Of Gcforestmentioning

confidence: 99%

Automobile Maintenance Modelling Using gcForest

Chen

Liu

Sun

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

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Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on automobile historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.

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“…This fact was a reason for developing new modifications of the DF and for applying it to several applications. In particular, Wang et al [19] proposed to apply the deep forest to forecast the current health state or to diagnostic health monitoring. Yang et al [25] applied the DF to solving the ship detection problem from thermal remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Weighted Deep Forest Classifier

Utkin¹,

Konstantinov²,

Chukanov³

et al. 2019

Preprint

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A modification of the confidence screening mechanism based on adaptive weighing of every training instance at each cascade level of the Deep Forest is proposed. The idea underlying the modification is very simple and stems from the confidence screening mechanism idea proposed by Pang et al. to simplify the Deep Forest classifier by means of updating the training set at each level in accordance with the classification accuracy of every training instance. However, if the confidence screening mechanism just removes instances from training and testing processes, then the proposed modification is more flexible and assigns weights by taking into account the classification accuracy. The modification is similar to the AdaBoost to some extent. Numerical experiments illustrate good performance of the proposed modification in comparison with the original Deep Forest proposed by Zhou and Feng.

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Deep forest based multivariate classification for diagnostic health monitoring

Cited by 7 publications

References 19 publications

Application of improved deep forest algorithm in image recognition

Application of improved deep forest algorithm in image recognition

Automobile Maintenance Modelling Using gcForest

An Adaptive Weighted Deep Forest Classifier

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