In recent years, credit scoring has received wide attention from financial institutions, with the rating accuracy influencing both risk control and profitability to a considerable extent. This paper presents a novel multi-stage self-adaptive classifier ensemble model based on statistical techniques and machine learning techniques, to improve the prediction performance. First, the multi-step data preprocessing is employed to process the original data into standardized data and generate more representative features. Second, base classifiers can be self-adaptively selected from the candidate classifier repository according to their performance in datasets, and their parameters are optimized by Bayesian optimization algorithm. Third, the ensemble model is integrated through these optimized base classifiers, and it can generate new features through multi-layer stacking and obtain the classifier weights in the ensemble model through the particle swarm optimization. The proposed model is applied to credit scoring to test its prediction performance. In the experimental study, three real-world credit datasets and four evaluation indicators are adopted for performance evaluation. The results show that compared to single classifier and other ensemble classification methods, the proposed model has better performance and better data adaptability. It proves the reliability and practicability of the proposed model, and provides effective decision support for relevant financial institutions.

With the emergence of the remanufacturing industry, scheduling problems related to remanufacturing systems have drawn considerable interest from scholars. There are usually two execution modes of remanufacturing for reprocessing components, i.e., replacement mode and repair mode. However, few studies have focused on the job shop scheduling problem that considers the trade-off between the two execution modes. Thus, a new job shop scheduling method with job families (JSS-JF) is proposed, which can handle the selection of appropriate execution modes for reprocessing components with different damaged conditions. It decomposes the scheduling problem into three sub-problems: (1) execution mode selection; (2) replacement job assignment and sequencing; and (3) repair machine assignment and operation sequencing. The scheduling objective is to minimize the total completion time and total cost of job families. To solve the proposed JSS-JF model, an extended artificial bee colony algorithm with a new three-dimensional encoding scheme is presented to find a near-optimal solution of the proposed JSS-JF model. The crossover and mutation operators, local search, and elite replacement strategy are also integrated in the proposed algorithm. The experiments are conducted for verifying the practicality and effectiveness of the presented algorithm by comparing with six baseline algorithms.