Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusions: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.

The economic circle around Beijing is a very important area that supports the development of Beijing-Tianjin-Hebei region in China. The economic growths of the economic circle around Beijing are deeply affected by the regional economic disparity. Besides the resources and environment problems, economic factors may be more prominent of regional sustainability. The objective of this paper is: (1) To find out what caused the regional economic disparity of Beijing-Tianjin-Hebei region; and (2) to assess the economic sustainable development status of the cities around Beijing, and give suggestions to narrow the regional disparity from the economic growth view. We used an assessment method based on the economic sustainable index system—the order relation method named G1 method, revised by standard deviation. The results showed that the fundamental reason for the differences lies in the disparity in production efficiency. The disparity in the regional economy increases the flow of talent to Beijing and Tianjin, so the labor productivity of Hebei Province will not improve. Thus, the economic gap among Hebei, Beijing, and Tianjin will continue to grow. This negative feedback will further exacerbate the economic growth problem. In terms of narrowing regional disparity, some implications are highlighted. Rational industrial structure, a high level of development in the manufacturing industry, and a high proportion of strategic emerging industries are important for the cities around Beijing to achieve high-level industry transfer, and the technological progress needed to promote economic growth.

Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

Assessing the default of customers is an essential basis for personal credit issuance. This paper considers developing a personal credit default discrimination model based on Super Learner heterogeneous ensemble to improve the accuracy and robustness of default discrimination. First, we select six kinds of single classifiers such as logistic regression, SVM, and three kinds of homogeneous ensemble classifiers such as random forest to build a base classifier candidate library for Super Learner. Then, we use the ten-fold cross-validation method to exercise the base classifier to improve the base classifier’s robustness. We compute the base classifier’s total loss using the difference between the predicted and actual values and establish a base classifier-weighted optimization model to solve for the optimal weight of the base classifier, which minimizes the weighted total loss of all base classifiers. Thus, we obtain the heterogeneous ensembled Super Learner classifier. Finally, we use three real credit datasets in the UCI database regarding Australia, Japanese, and German and the large credit dataset GMSC published by Kaggle platform to test the ensembled Super Learner model’s effectiveness. We also employ four commonly used evaluation indicators, the accuracy rate, type I error rate, type II error rate, and AUC. Compared with the base classifier’s classification results and heterogeneous models such as Stacking and Bstacking, the results show that the ensembled Super Learner model has higher discrimination accuracy and robustness.

The burgeoning significance of topic mining for social media text has intensified with the proliferation of social media platforms. Nevertheless, the brevity and discreteness of social media text pose significant challenges to conventional topic models, which often struggle to perform well on them. To address this, the paper establishes a more precise Position-Sensitive Word-Embedding Topic Model (PS-WETM) to adeptly capture intricate semantic and lexical relations within social media text. The model enriches the corpus and semantic relations based on word vector similarity, thereby yielding dense word vector representations. Furthermore, it proposes a position-sensitive word vector training model. The model meticulously distinguishes relations between the pivot word and context words positioned differently by assigning different weight matrices to context words in asymmetrical positions. Additionally, the model incorporates self-attention mechanism to globally capture dependencies between each element in the input word vectors, and calculates the contribution of each word to the topic matching performance. The experiment result highlights that the customized topic model outperforms existing short-text topic models, such as PTM, SPTM, DMM, GPU-DMM, GLTM and WETM. Hence, PS-WETM adeptly identifies diverse topics in social media text, demonstrating its outstanding performance in handling short texts with sparse words and discrete semantic relations.