## 1 Introduction  
Data-driven constitutes a form of actuarial analysis in which data serves as the guiding principle of reality. However, in practical applications, it can be observed that the levels of different data types vary widely, which actually indicates a serious imbalance in the data set. In the field of industry and fault prediction, this imbalance ratio ranges from 1:1000 to 1:100,000, and in some cases of precision systems engineering [1], the imbalance ratio is even more obvious. From a disciplinary perspective, limited numbers of samples tend to exhibit greater representativeness, especially in the context of disease data, failure data, and defect data, highlighting the important utility of these samples. Serious damage to the balance of data sets will lead to overfitting of some data, resulting in the concentration of sample classification, the decline of robustness of classification methods, and the decline of data analysis accuracy [2]. Oversampling techniques such as majority-weighted Minority Oversampling (MWMOTE) can effectively deal with a small number of characteristics of fault samples in unbalanced data and can also produce a better processing effect on noise. For example, the KNN method can effectively deal with local noise when the value of neighborhood parameter k is reasonable, but without prior experience, the KNN method may lead to insufficient or excessive recognition [3]. The double challenge of an unbalanced data set with noise poses new challenges to both the KNN algorithm and the original MWMOTE algorithm, so we propose an improved algorithm. Fuzzy-Rough Instance Selection (FRIS) and Iterative Noise Filter Based on the Fusion of Classifiers (INFFC), it realizes adaptive noise processing and unbalanced data processing, uses Euclidean distance and density screening to eliminate true noise and suspected noise, and enriches the original unbalanced samples by over-sampling technology. In this paper, the algorithm is renamed as a majority-class weighted minority-class oversampling algorithm (MWMOTE-FRIS-INFFC) based on the fusion of fuzzy rough instance selection and iterative noise classification fusion filtering. Moreover, it avoids the generation of new noise. Moreover, it can also overcome the expansion of inter-class imbalance and intra-class imbalance. Through the experimental results, it can be seen that the algorithm proposed in this study is due to other common algorithms. Oversampling technology constitutes a countermeasure algorithm addressing the imbalance of data in the realm of data analysis [4]. Initially, oversampling techniques employed methods such as data interpolation, data fusion, and dataset expansion to augment the count of minor samples, thereby attaining a balanced dataset. Subsequently, diverse oversampling techniques have been introduced for various domains, encompassing random oversampling, synthetic minority oversampling, adaptive synthetic sampling [5], safety level synthetic minority oversampling, and majority weighted minority oversampling. Nevertheless, despite these advancements, oversampling techniques applied to data in the domain of assembly still exhibit certain shortcomings. Even the aforementioned algorithms possess deficiencies in their handling of public datasets [6], which are outlined as follows:  
(1) Partial oversampling technology introduces data volume to solve the imbalance between classes, but there is still a lot of noise in the added data, resulting in increased overlap and ambiguity between data classes. (2) The experimental results of some algorithms are relatively good, but the practical application requires a lot of time and hardware support, resulting in insufficient universality.