Classifying Non-linear Gene Expression Data Using a Novel Hybrid Rotation Forest Method

Lu, Huijuan; Meng, Yaqiong; Yan, Ke; Xue, Yu; Gao, Zhigang

doi:10.1007/978-3-319-63315-2_64

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Ensemble or hybrid machine learning techniques are important solutions to raise the prediction accuracy of traditional machine learning techniques [20][21][22][23][24]. Tang et al [25] proposed a hybrid ensemble learning framework to forecast nuclear energy consumption patterns.…”

Section: Related Workmentioning

confidence: 99%

Tunnel Surface Settlement Forecasting with Ensemble Learning

Yan

Dai

et al. 2019

Sustainability

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Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency.

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