In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine learning methods have become mainstream. However, although traditional machine learning methods perform well in fraud detection tasks, the lack of interpretability and class imbalance issues have always been pain points that are difficult to resolve for such methods. Unlike traditional methods, the belief rule base, as a rule-based expert system model, can integrate expert knowledge and has excellent interpretability. In this paper, we propose an innovative ensemble BRB (belief rule base) model to solve the credit card fraud detection problem by combining an ensemble learning framework with the BRB model. Compared with traditional machine learning methods, the proposed model has the advantage of high interpretability. And compared with traditional BRB models, the ensemble framework enables better performance in dealing with highly imbalanced classification tasks. In an experimental study, two datasets of credit card fraud detection from Kaggle are used to validate the effectiveness of this work. The results show that this new method can achieve excellent performance in the application of fraud detection and is capable of effectively mitigating the impact of an imbalanced dataset.

Learning emotion assessment is a non-negligible step in analyzing learners’ cognitive processing. Data are the basis of the learning emotion assessment. However, the existing learning emotion assessment models cannot balance model accuracy and interpretability well due to the influence of uncertainty in the process of data collection and model parameter errors. Given the above problems, a new learning emotion assessment model based on evidence reasoning and a belief rule base (E-BRB) is proposed in this paper. First, the transformation matrix is introduced to transform multiple emotional indicators into the same standard framework and integrate them, which keeps the consistency of information transformation. Second, the relationship between emotional indicators and learning emotion states is modeled by E-BRB in conjunction with expert knowledge. In addition, we employ the projection covariance matrix adaptation evolution strategy (P-CMA-ES) to optimize the model parameters and improve the model’s accuracy. Finally, to demonstrate the effectiveness of the proposed model, it is applied to emotion assessment in science learning. The experimental results show that the model has better accuracy than data-driven models such as neural networks.

Accurately identifying bearing faults in aeroengines is crucial for maintaining their lifespan and cost. However, most current models are black-box models, such as deep learning models such as deep neural networks. The decision-making process of these models is more complex and lacks interpretability, which results in insufficient credibility of the results. Furthermore, data collected in real industrial environments can suffer from unbalanced sample categories. Moreover, the models can suffer from local ignorance in the prediction process. These problems can lead to a decrease in the prediction accuracy of the model. Therefore, a fault diagnosis method based on the interpretable belief rule base with a dynamic power set (D-HBRBP-I) is proposed in this study. First, a diagnostic model based on a belief rule base with a dynamic power set was used to address the problem of sample category imbalance and local ignorance. Second, optimizing the model via the P-CMAES algorithm with interpretability constraints can ensure the interpretability of the model after optimization. Finally, experiments were conducted on an aeroengine-bearing dataset. The results show that the proposed model effectively solves the above problem while achieving 99% accuracy.

Rolling bearings are crucial components in industrial applications, forming the core of modern rotating machinery. The belief rule base with interval structure (IBRB-r) is a rule-based expert system designed to describe the causality of bearing faults. It is mainly applicable in situations where data within the intervals are relatively concentrated. However, in practical industrial applications, bearing operation data is often dispersed.This leads to uneven data distribution within the reference interval, affecting the model’s accuracy. Additionally, the interpretability of the parameters decreases after the IBRB-r model is optimized. To address these issues, an adaptive rule matching interval structured belief rule (IBRB-Di) is proposed for bearing fault diagnosis.First, a rule matching method that can be dynamically computed within an reference interval is introduced.Also, a new computation method for the weights of rules activation within an interval is presented.These methods adapt to the dispersion of data and simplify the number of rules. Second, the projected covariance matrix adaptive evolution strategy with expert constraints (P-CMA-ES) is adopted to optimize the model.This aims to balance accuracy and interpretability. Finally, the method is experimentally validated using the Case Western Reserve University bearing dataset. This verification confirms the effectiveness of the proposed method.

The operational condition of rolling bearings is essential to the reliability of industrial machinery, making fault diagnosis a critical research topic. Although deep learning has gained widespread attention in this domain, its black-box character and reliance on a large number of training samples limit its practical applicability. In contrast, knowledge-driven intelligent diagnostic models have gained increasing interest due to their superior interpretability and robustness under small-sample conditions. The Belief Rule Base (BRB) model is a representative example of such interpretable methods. However, the conventional BRB models struggle to process continuous signals, limiting their effectiveness in real-world bearing fault diagnosis. This study proposes a novel Vibration-Enhanced Belief Rule Base (VE-BRB) model designed to address this limitation. First, the window feature extraction method is used to preprocess the continuous vibration signal. Bearing fault features are extracted from low-frequency and high-frequency to construct an energy matrix representation. Thereby, the original continuous vibration signal can be effectively mapped to the rule matching space. Second, the model is reasoned through an evidential reasoning (ER) algorithm. This guarantees the interpretability of the model. Finally, the projection covariance matrix adaptation evolution strategy (P-CMA-ES) is employed as the optimization process. To validate the effectiveness of the proposed method, the test was performed using bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology under various conditions.

The accurate diagnosis of bearing faults is paramount for the reliable operation of industrial machinery. The belief rule base (BRB), recognized for its interpretability, has consequently been extensively applied in the domain of fault diagnosis. Nevertheless, practical industrial scenarios present considerable challenges, including noise interference, the demand for detailed fault information, and the limited availability of labeled samples, which collectively constrain the applicability of BRB methods. To address these limitations, the present study introduces a semisupervised multilayer belief rule base (SM-BRB) approach. Initially, the superlet transform (SLT) is leveraged to extract salient structural features and to enhance robustness against noise. Drawing inspiration from real-world diagnostic practices, a hierarchical framework is constructed to enable refined fault identification. Furthermore, a semisupervised learning paradigm is employed to capitalize on latent information contained within a surplus of unlabeled data, thereby enabling the model to sustain high diagnostic accuracy despite the scarcity of labeled examples. Experimental findings under noisy conditions substantiate the efficacy and interpretability of the proposed SM-BRB model.