Research and application of XGBoost in imbalanced data

Zhang, Ping; Jia, Yiqiao; Shang, Youlin

doi:10.1177/15501329221106935

Cited by 49 publications

(27 citation statements)

References 11 publications

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“…Thus, models like SVR and XGBoost might not be as effective in modeling patterns without extensive feature engineering 56,57 .…”

Section: Discussionmentioning

confidence: 99%

Data analysis and Forecasting of Tuberculosis and HIV co-infection: Exploring models from classical statistics to machine learning

Abade,

Porto,

Scholze

et al. 2024

Preprint

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Considering the complexity and severity of TB/HIV coinfection, accuracy in forecasting future trends is crucial for the efficient allocation of public health resources and the development of intervention strategies. This study explores the application of predictive models, ranging from classical statistical approaches to machine learning techniques, to analyze the time series of TB/HIV coinfection case notifications stratified for men, women, and the general population. Traditional models using Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) methods were initially applied, providing a basis for understanding the overall temporal trend and the presence of seasonality. Subsequently, machine learning models, including Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Deep Neural Networks such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), were tested to find the best model to capture the complex dynamics of TB/HIV coinfection and the inherent non-linearities in the data. Performance evaluation, based on error metrics such as MSE, MAE, and sMAPE, revealed that Deep Learning models, especially Bidirectional LSTM and CNN combined with LSTM, significantly outperformed the classical statistical methods, demonstrating the effectiveness of these techniques for modeling TB/HIV coinfection time series and, consequently, providing more accurate forecast models.

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“…Thus, models like SVR and XGBoost might not be as effective in modeling patterns without extensive feature engineering 56,57 .…”

Section: Discussionmentioning

confidence: 99%

Data analysis and Forecasting of Tuberculosis and HIV co-infection: Exploring models from classical statistics to machine learning

Abade,

Porto,

Scholze

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…These nonlinear relationships can be explored with ML algorithms, especially those that are capable of detecting complicated interactions, such as random forest and XGBoost. 189,190 Furthermore, sensitivity analysis can aid in determining the generalizability and durability of an ML model. Researchers can determine the stability and reliability of the predictions by examining the performance of the model under different scenarios and by perturbing input variables.…”

Section: Sensitivity Of Machine Learning Models To Input Variablesmentioning

confidence: 99%

“…Some features of the biomass may interact synergistically, resulting in nonlinear effects on the synthesis of the product. These nonlinear relationships can be explored with ML algorithms, especially those that are capable of detecting complicated interactions, such as random forest and XGBoost 189,190 . Furthermore, sensitivity analysis can aid in determining the generalizability and durability of an ML model.…”

Section: Feature Importance and Sensitivity Analysismentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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“…Construction of Training, Validation, and Test Data Sets. Common approaches to address imbalanced data include undersampling, 24 oversampling, 25 ensemble methods, 26 etc. Undersampling techniques may cause information loss, and oversampling may lead to overfitting.…”

Section: Benchmark Data Setsmentioning

confidence: 99%

AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention

Li,

Zou,

Jia

et al. 2023

J. Chem. Inf. Model.

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Antimicrobial peptides (AMPs) are small molecular polypeptides that can be widely used in the prevention and treatment of microbial infections. Although many computational models have been proposed to help identify AMPs, a high-performance and interpretable model is still lacking. In this study, new benchmark data sets are collected and processed, and a stacking deep architecture named AMPpred-MFA is carefully designed to discover and identify AMPs. Multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (LSTM) network and a convolutional neural network (CNN). The effectiveness of AMPpred-MFA is verified through five independent tests conducted in batches. Experimental results show that AMPpred-MFA achieves a state-of-the-art performance. The visualization interpretability analyses and ablation experiments offer a further understanding of the model behavior and performance, validating the importance of our feature representation and stacking architecture, especially the multihead attention mechanism. Therefore, AMPpred-MFA can be considered a reliable and efficient approach to understanding and predicting AMPs.

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Research and application of XGBoost in imbalanced data

Cited by 49 publications

References 11 publications

Data analysis and Forecasting of Tuberculosis and HIV co-infection: Exploring models from classical statistics to machine learning

Data analysis and Forecasting of Tuberculosis and HIV co-infection: Exploring models from classical statistics to machine learning

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention

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