Bond Default Prediction Based on Deep Learning and Knowledge Graph Technology

Ma, Cuiluan; Hongyan, Sun; Wang, Shaofan; Lu, Shengliang; Li, Jingyan

doi:10.1109/access.2021.3052054

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…It has only made achievements in a few fields. The more representative ones are the field of traditional Chinese medicine (Yu et al, 2017;Xiong et al, 2021), the financial field (Ma et al, 2021), and the field of computer technology (Chen, 1992).…”

Section: Application Of the Knowledge Graphmentioning

confidence: 99%

Research Review of the Knowledge Graph and its Application in Power System Dispatching and Operation

Chen

Lu²,

Pan³

et al. 2022

Front. Energy Res.

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With the construction of a new power system and the proposal of a double carbon goal, power system operation data are growing explosively, and the optimization of power system dispatching operation is becoming more and more complex. Relying on traditional pure manual dispatching is difficult to meet the dispatching needs. The emerging knowledge graph technology in the field of the artificial intelligence technology is one of the effective methods to solve this problem. Because the topological structure of the power system itself is consistent with the relational structure of graph theory, through the establishment of a relevant knowledge graph, the real operating state of the power system can be restored to the maximum extent by effectively preserving the correlation implicit in the data. Meanwhile, expressing the hidden knowledge in the power system dispatching operation in the form of a knowledge graph has become the focus of research at home and abroad. This study summarizes the development of the knowledge graph technology from the aspects of knowledge extraction, knowledge representation learning, knowledge mining, knowledge reasoning, knowledge fusion, and the application of knowledge graph and introduces the application and prospect of knowledge graph in the power system dispatching operation from the aspects of the auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience, and super regulation parameters.

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Section: Application Of the Knowledge Graphmentioning

confidence: 99%

Research Review of the Knowledge Graph and its Application in Power System Dispatching and Operation

Chen

Lu²,

Pan³

et al. 2022

Front. Energy Res.

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“…With the continuous development of information technology, knowledge graph technology provides new ideas for risk analysis, and at present, knowledge graph risk analysis mainly focuses on the fields of urban energy, enterprise credit, medical treatment, gas accident, etc. For example, Chi et al [19] combined knowledge graph with deep learning algorithms to utilize the prediction of bond defaults based on multi-source information and macroeconomic data. Alam et al [20] introduced a framework based on knowledge graph and XGBoost for loan default prediction using a household credit default risk dataset.…”

Section: Introductionmentioning

confidence: 99%

Construction of Knowledge Graph of the Elevator Safety Accidents and Analysis of Key Risk Factors Based on KG-DEMATEL-ISM-MICMAC Method

Shi,

2024

IEEE Access

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The safety of elevator, as a closely related equipment in people's daily life, is getting more and more attention. Once an elevator accident occurs, it will bring strong social impact. It is important to control the elevator safety risk from the source to reduce the accidents. This article combines the risk factor analysis methods of Knowledge Graph (KG), Decision Making Experimentation and Evaluation Laboratory (DEMATEL), Interpretative Structural Modeling (ISM) method, and Multiplication of Cross Matrices (MICMAC) to analyses elevator safety accidents in a data-driven manner in order to minimize the reliance on experts. Firstly, the causal network of accident occurrence is extracted from the accident reports based on the knowledge graph approach. Secondly, complex correlations in the causal network are quantified from the application of DEMATEL. Then, the ISM method was used to construct a hierarchical structure of elevator safety risk factors, on the basis of which the MICMAC method was combined to classify the types of risk factors and analyses the degree of influence of risk factors at each level. Finally, targeted preventive measures for elevator safety accidents are proposed based on the results of the model analysis.

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“…C HINA corporate bond market is the largest bond market, with over 306 billion USD 1 . According to the Fitch Ratings, one of the big three credit rating agencies, China corporate bond default rate touches a record high in 2022 2 .…”

Section: Introductionmentioning

confidence: 99%

“…That means it is a great challenge for highly leveraged firms to repay their principal and interest, resulting in an increasing number of bond defaults. Corporate bond default prediction can be formulated as a two-class classification problem and has received considerable attention [1].…”

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

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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.

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Bond Default Prediction Based on Deep Learning and Knowledge Graph Technology

Cited by 10 publications

References 16 publications

Research Review of the Knowledge Graph and its Application in Power System Dispatching and Operation

Research Review of the Knowledge Graph and its Application in Power System Dispatching and Operation

Construction of Knowledge Graph of the Elevator Safety Accidents and Analysis of Key Risk Factors Based on KG-DEMATEL-ISM-MICMAC Method

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

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