Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction

Guo, Ke; Shen, Changchun; Zhou, Xiaokang; Ren, Siming; Hu, Min; Shen, Minxue; Chen, Xiang; Guo, Haifu

doi:10.1109/tits.2022.3172206

Cited by 9 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a promising method for estimating the E of rock. Recently, the XGBoost and RF algorithms have shown great potential to improve prediction accuracy and have been successfully applied in many fields, such as electricity consumption forecasting [39,40], infectious disease prediction [41,42], mining maximum subsidence prediction [43,44], and heavy metal contamination prediction [45,46]. XGBoost and RF are two efficient ensemble methods that combine multiple homogeneous weak learners in certain ways to reduce overfitting.…”

Section: Yearmentioning

confidence: 99%

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Lin,

Liu,

Zhao

et al. 2023

Mathematics

View full text Add to dashboard Cite

Accurate estimation of the elastic modulus (E) of rock is critical for the design of geotechnical projects such as mining, slopes, and tunnels. However, the determination of rock mechanical parameters usually involves high budget and time requirements. To address this problem, numerous researchers have developed machine learning models to estimate the E of rock. In this study, two novel hybrid ensemble learning models were developed to estimate the E of rock by optimizing the extreme gradient boosting (XGBoost) and random forest (RF) algorithms through the dwarf mongoose optimization (DMO) approach. Firstly, 90 rock samples with porosity, dry density, P-wave velocity, slake durability, and water absorption as input indicators were collected. Subsequently, the hyperparameters of XGBoost and RF were tuned by DMO. Based on the optimal hyperparameters configuration, two novel hybrid ensemble learning models were constructed using the training set (80% of the data). Finally, the performance of the developed models was evaluated by the coefficient of determination (R2 score), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF) on the test set (20% of the data). The results show that the DMO-RF model achieved the best comprehensive performance with an R2 score of 0.967, RMSE of 0.541, MAE of 0.447, and VAF of 0.969 on the test set. The dry density and slake durability were more influential indicators than others. Moreover, the convergence curves suggested that the DMO-RF model can reduce the generalization error and avoid overfitting. The developed models can be regarded as viable and useful tools in estimating the E of rock.

show abstract

Section: Yearmentioning

confidence: 99%

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Lin,

Liu,

Zhao

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The most-used techniques in these works are Markov chains [91,92,96,97,108], differential equations [28,110,111], contact networks [9,92,93,95,98], and machine-learning algorithms [93,96,99,101,102,104,105,107,113,115]. Different studies use mathematical models that range from statistics on social media [24] to machine learning [103] and have different applications in the health field.…”

Section: In Human Epidemiological Analysis What Are the Techniques An...mentioning

confidence: 99%

“…The speed at which diseases travel through populations depends not only on the effective distance between locations [103], but also on how the disease is transmitted between people in those locations. This allows us to understand more about the transmission of diseases.…”

mentioning

confidence: 99%

Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

et al. 2022

View full text Add to dashboard Cite

There are different bovine infectious diseases that show economic losses and social problems in various sectors of the economy. Most of the studies are focused on some diseases (for example, tuberculosis, salmonellosis, and brucellosis), but there are few studies on other diseases which are not officially controlled but also have an impact on the economy. This work is a systematic literature review on models (as a theoretical scheme, generally in mathematical form) used in the epidemiological analysis of bovine infectious diseases in the dairy farming sector. In this systematic literature review, criteria were defined for cattle, models, and infectious diseases to select articles on Scopus, IEEE, Xplorer, and ACM databases. The relations between the found models (model type, function and the proposed objective in each work) and the bovine infectious diseases, and the different techniques used and the works over infectious disease in humans, are presented. The outcomes obtained in this systematic literature review provide the state-of-the-art inputs for research on models for the epidemiological analysis of infectious bovine diseases. As a consequence of these outcomes, this work also presents an approach of EiBeLec, which is an adaptive and predictive system for the bovine ecosystem, combining a prediction model that uses machine-learning techniques and an adaptive model that adapts the information presented to end users.

show abstract

“…SS aims to select ensemble classifiers based on average performance of base classifiers on a validation set, which is constructed from training data set. Typical SS methods are BoostForest [8], AdaBoost [9] and XGBoost [10]. SS is all-sampleoriented, that all test samples share the same ensemble classifier.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

Wang

Zhang

Yan³

2023

IEEE Access

View full text Add to dashboard Cite

China corporation bond default prediction is important and can be formulated as an imbalance classification problem solved by static ensemble classifiers. However, dynamic ensemble selection (DES) classifiers have not been applied to this typical problem in the context of business research. DES classifiers are capable of selecting an ensemble classifier for each test sample, leading to better classification performance than static ensemble classifiers. Most existing DES classifiers can not address imbalance classification optimally and only use single criteria of competent for classifier selection, resulting in sub-optimal classification performance. In this paper, we propose an enhanced DES classifier, named META-DES-Diversity, that inherits strengths of data sampling, meta-learning, criteria of diversity, and dynamic weighting fusion scheme to alleviate such limitations. Specifically, the synthetic minority over-sampling technique is initially used to balance the training set before generating a candidate classifier pool. To select an ensemble classifier with a highly competent, the meta-learning framework META-DES is utilized to consider multiple criteria of competence. In complement with the meta-leaning framework, a two-phase selection strategy is utilized to perform competence and diverse ensemble classifier selection. Note that a competencedriven decision fusion scheme is employed to effectively fuse classification results from selected ensemble classifiers. Experiments on 14 two-class imbalanced data sets from the KEEL repository and one self-collected China corporation bond data set show the effectiveness and superiority of the proposed enhanced DES classifier.

show abstract

Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction

Cited by 9 publications

References 40 publications

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

Contact Info

Product

Resources

About