Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5′ and CART algorithms

Hamze‐Ziabari, Seyed Mahmood; Bakhshpoori, Taha

doi:10.1016/j.asoc.2018.03.052

Cited by 52 publications

(21 citation statements)

References 36 publications

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“…The authors [31] have developed three ensemble models based on bagging ensemble technique M5' ensemble, CART ensemble, and hybrid M5' and CART ensemble to find the parameters of ground peak time-domain. They have tested the developed model against the statistical error parameters.…”

Section: Related Workmentioning

confidence: 99%

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

et al. 2020

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Major depressive disorder (MDD) is a persistent psychiatric mood disorder that is prevalent from a few weeks to a few months, even for years in the worst cases. It causes sadness, hopelessness in the individuals; sometimes, it forces them to hurt themselves. In severe cases, MDD can even lead to the death of the individual. It is challenging to diagnose MDD as it co-occurs with many other disorders (Co-Morbid) and many other reasons such as mobility, lack of motivation, and cost. The way to diagnose MDD is usually high ended that is challenging for the regular clinicians to diagnose. Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model. The data collected has been cleaned with a preprocessing technique, and feature selection are performed using wrapper based methods; moreover, in the final step, a stacking based ensemble learning model is implemented to classify the MDD patients. Furthermore, KNN Imputation is implemented for preprocessing, Random Forest-Based Backward Elimination for feature selection and multi-layer perceptron, SVM and Random Forest as low-level learners in stacking generalization model. The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers. INDEX TERMS K-nearest neighbors, major depressive disorder, multilayer perceptron, random forest, random forest-based feature elimination, stacking generalization and support vector machine.

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Section: Related Workmentioning

confidence: 99%

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

et al. 2020

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“…Thirdly, CART (classification and regression tree) proposed by Breiman et al in 1984. Is currently a quite popular decision tree algorithm [23]. In this paper, the CART algorithm is used as the decision tree algorithm for the construction of the random forest.…”

Section: E Random Forest Modelmentioning

confidence: 99%

The Application of the Machine Learning Method in Electromyographic Data

Liu

Tang

et al. 2020

IEEE Access

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This paper studies the application of machine learning in the analysis and diagnosis of electromyography data. Firstly, 2,352 electromyography examination reports have been recorded from Sichuan Provincial Hospital of Traditional Chinese Medicine for ten months. The data cleaning has been conducted based on the specific-designed inclusion criteria. Next, two data sets have been established, containing 575 facial motor nerve conduction study reports and 233 auditory brainstem response reports, respectively. And then, four machine learning algorithms including random forest, linear regression, support vector machine and logistic regression have been employed to the data sets. The performance comparisons of accuracy and recall rate among different algorithms indicate that the random forest algorithm has the optimal performance over the other two in both data sets. Moreover, the comparisons have been carried out in the cases with and without deviation standardization for each algorithm, and the results demonstrate that the deviation standardization has a certain effect on the accuracy improvement. Additionally, it is found that the random forest algorithm can present the ranking of the features in order of importance. Consequently, the random forest is proven to be an optimal algorithm for computer-aided diagnosis systems. Furthermore, it is worth mentioning that the feature ranking in order of importance can facilitate clinical diagnosis and has a certain clinical potential in diagnosis and diagnostic assessment.

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“…In data mining, CART was introduced as an effective nonparametric algorithm for forecasting issues, including regression and classification. Additionally, it is also known as a robust decision tree algorithm for forecasting problems [44]. Inspired by the development of trees in nature, CART's operational principles are developed based on mapping data [45].…”

Section: Cartmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5′ and CART algorithms

Cited by 52 publications

References 36 publications

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

The Application of the Machine Learning Method in Electromyographic Data

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

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