XGBoost Model and Its Application to Personal Credit Evaluation

Li, Hua; Cao, Y.; Li, Siwen; Zhao, Jianbin; Sun, Yutong

doi:10.1109/mis.2020.2972533

Cited by 89 publications

(39 citation statements)

References 7 publications

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“…In the selection of ML models we focused on more flexible algorithms such as extreme gradient boosting (XGBoost), support vector regressor (SVR), and ANNs as they have sophisticated error‐handling capabilities compared to other ML algorithms. XGBoost performs better than any other ML algorithm in data modeling and has already been applied successfully in numerous fields (Georganos et al., 2018; Islam, Islam, Ahasan, Mia, & Haque, 2021; Li, Cao, Li, Zhao, & Sun, 2020; Oh, Ham, Lee, & Kim, 2019). In several studies, it showed better performance and took less time than other algorithms, including the SVM, RF, and ANNs (Georganos et al., 2018; Munkhdalai, Wang, Park, & Ryu, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Incorporating spatial information in machine learning: The Moran eigenvector spatial filter approach

Islam

Lee

et al. 2022

Transactions in GIS

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Spatial statistical models are highly effective for modeling geospatial data as they consider spatial information of geographic spaces and other non‐spatial covariates, enabling them to minimize spatial autocorrelation by addressing spatial dependence. In contrast, machine learning (ML) models are highly effective for predicting non‐spatial data, but they are not as effective for modeling and predicting geospatial data because of spatial autocorrelation issues. One of the frequently reported limitations of ML models for geospatial data modeling is that there is no standard method of incorporating spatial information of geographic space into the model, and consequently they cannot minimize spatial autocorrelation. In this study, we have presented a local spatial information‐embedded ML method capable of minimizing spatial autocorrelation by addressing spatial dependence while predicting a geospatial phenomenon. Our study applied the eigenvector spatial filter method to extract approximated eigenvectors from spatial coordinates and embed them within ML models as a set of vectors along with the selected non‐spatial covariates. We have also presented a comparison of relative prediction performance between traditional spatial statistical and ML‐based models. The experiment demonstrates that incorporating spatially filtered eigenvectors to represent spatial information in ML model specification significantly improves the prediction performance.

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Section: Literature Reviewmentioning

confidence: 99%

Incorporating spatial information in machine learning: The Moran eigenvector spatial filter approach

Islam

Lee

et al. 2022

Transactions in GIS

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“…The CL strategy is a combination of both DL and AL strategies, or combination of DL strategy with another DL, and a combination of AL with another AL strategy. In addition, it can also be a combination of ensemble method with AL strategy, or combination of ensemble method with DL strategy, or it can be a combination of both hybrid and ensemble methods [57], [58]- [60]. For example, based on a combination of data rebalancing and Extreme Gradient Boosting (XGBoost), a unique form of malicious synchrophasors detector is developed [61].…”

Section: Strategies In Handling Imbalanced Datamentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.

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“…For the fifth article entitled "XGBoost Model and Its Application to Personal Credit Evaluation" by Li et al, 7 they investigate the application of eXtreme Gradient Boosting (XGB) method to the credit evaluation problem based on big data. In particular, they first study the theoretical modeling of the credit classification problem using XGB algorithm, then apply the XGB model to the personal loan scenario based on the open dataset from Lending Club Platform in the USA.…”

Section: Editorial Editorialmentioning

confidence: 99%

Special Issue on AI and FinTech: The Challenge Ahead

et al. 2020

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& IT IS OUR pleasure to share with you this special issue on AI and Fintech, which includes 17 articles published in the March/April (8 articles) and May/June (9 articles) issues of IEEE Intelligent Systems (IS).After our announcement in early September 2019 for this special issue on AI and Fintech for IS, we received a larger volume of manuscripts than anticipated by the January 2020 deadline. These worldwide submissions included both academic researchers and practitioners in IT and financial industries. After a long round of revisions from independent, anonymous referees that ultimately led to the current, official versions of the articles published in both issues, we would like to first and foremost thank all the contributors and the anonymous referees for their hard work on this project. Second, we give our sincere gratitude to the supportive effort of the IS team led

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XGBoost Model and Its Application to Personal Credit Evaluation

Cited by 89 publications

References 7 publications

Incorporating spatial information in machine learning: The Moran eigenvector spatial filter approach

Incorporating spatial information in machine learning: The Moran eigenvector spatial filter approach

Survey on Highly Imbalanced Multi-class Data

Special Issue on AI and FinTech: The Challenge Ahead

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