MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

Dong, Yunyun; Yang, Wenkai; Wang, Jiawen; Zhao, Juanjuan; Qiang, Yan

doi:10.3390/app9173589

Cited by 14 publications

(8 citation statements)

References 44 publications

(75 reference statements)

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“…The gcForest also has been applied in the medical field. Dong et al (2019) [23] proposed a multi-weighted gcForest algorithm to build a lung adenocarcinoma staging prediction model. The weights of random forests (RF) in the standard gcForest are the same, which might be not reasonable.…”

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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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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“…At the same time, the deep forest algorithm can be used to extract multi-level amplitude features and multi-level dense scale-invariant feature transformation features, amplitude features, and realize fault recognition in multi-feature mode [17][18][19]. In addition, some scholars used machine learning methods to improve the deep forest model, solving the problems of the long characteristics of single-sample data of mechanical equipment vibration signals and the high cost of deep forest model data processing, and realized the fault diagnosis of mechanical equipment under small training samples [20]. The bearing vibration signal data samples of coal mine main fans cover a large number of sample points, mostly 1000-4000 data points.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of Rolling Bearing Based on Wavelet Packet Analysis and Deep Forest

Zhang

Wang

et al. 2022

Symmetry

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The frequent accidents caused by the main fan motor in coal mines have exposed the safety hazards of rolling bearings. When a rolling bearing fails, its symmetry is broken, resulting in a rapid decline in its safety performance and posing a great threat to the main fan. Therefore, accurate rolling bearing fault diagnoses are the key to ensuring the safe and durable operation of main fans. Thus, in this paper, we propose a new fault diagnosis method of rolling bearing based on wavelet packet analysis and deep forest algorithm. Firstly, experiments were conducted under different health states to guarantee the diversity of data relating to the rolling bearing’s main fan and then to ensure the accuracy of the fault diagnosis under different health states. On the basis of the collected vibration signal data, we conducted the wavelet packet analysis method to extract the characteristics of the vibration signal and obtained a feature vector that characterizes the health of the bearing. After that, the extracted feature vector was used as the feature vector of the deep forest algorithm to train the deep forest diagnosis model and determine the location and fault type of the bearing fault. Finally, the proposed method in this paper was validated with real-time monitoring data of a main ventilation fan and compared with other diagnostic algorithms, which not only verified the diagnostic capability of deep forest in handling small samples, but also verified the diagnostic capability of the fault diagnosis model. In summary, the proposed fault diagnosis approach is promising in real coal mine main fans.

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“…The small data size and imbalanced data problems have been addressed in [22], where an enhanced algorithm to handle the data was proposed, combining traditional techniques with deep learning methods. The results obtained confirmed that deep learning enhances performance; in addition to that, methylation data were suggested to be effectively used to improve diagnosis of cancer.…”

Section: Introductionmentioning

confidence: 99%

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

El-Nabawy¹,

Belal

El-Bendary

2021

Mathematics

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Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the same efficiency. Many methods have been used for breast cancer subtype classification. Some use single data source, while others integrate many data sources, the case that results in reduced computational performance as opposed to accuracy. Breast cancer data, especially biological data, is known for its imbalance, with lack of extensive amounts of histopathological images as biological data. Recent studies have shown that cascade Deep Forest ensemble model achieves a competitive classification accuracy compared with other alternatives, such as the general ensemble learning methods and the conventional deep neural networks (DNNs), especially for imbalanced training sets, through learning hyper-representations through using cascade ensemble decision trees. In this work, a cascade Deep Forest is employed to classify breast cancer subtypes, IntClust and Pam50, using multi-omics datasets and different configurations. The results obtained recorded an accuracy of 83.45% for 5 subtypes and 77.55% for 10 subtypes. The significance of this work is that it is shown that using gene expression data alone with the cascade Deep Forest classifier achieves comparable accuracy to other techniques with higher computational performance, where the time recorded is about 5 s for 10 subtypes, and 7 s for 5 subtypes.

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MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

Cited by 14 publications

References 44 publications

Automobile Maintenance Modelling Using gcForest

Automobile Maintenance Modelling Using gcForest

A Fault Diagnosis Method of Rolling Bearing Based on Wavelet Packet Analysis and Deep Forest

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

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