## 5 Conclusions  
As the various algorithms in the realm of big data continue to evolve and advance, scientists have observed that research conducted at the foundational level of data frequently offers a more telling representation of scientific veracity. Within the industrial sector, the binary classification issue arises frequently, yet its outcomes are often markedly imbalanced. Although a minor proportion of samples possess analytical significance, their scant representation can readily lead to inaccuracies in data classification algorithms. Furthermore, from a pragmatic perspective, data noise emerges as a pivotal concern in image algorithmic applications. Thus, the key findings of this study can be condensed as follows:  
(1) At present, the processing methods of unbalanced samples are mostly oversampling and undersampling. Among them, the over-sampling technology has a better effect on the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Set** & **Meas** & **ROS** & **SMOTE** & **ADASYN** & \begin{tabular}{c} **Cluster-** \\ **SMOTE** \\ \end{tabular} & **A-SUWO** & \begin{tabular}{c} **MWMOTE-** \\ **FRIS-INFFC** \\ \end{tabular} \\ \hline \multirow{3}{*}{Ecoli3 (20.0\%)} & Precision & 0 & 0 & 0 & 1 & 0 & 4 \\ \cline{2-7}  & G-mean & 0 & 0 & 0 & 0 & 0 & 5 \\ \cline{2-7}  & F-measure & 0 & 0 & 0 & 1 & 0 & 4 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Ds1.100 \\ (31.72\%) \\ \end{tabular} } & Precision & 0 & 0 & 1 & 0 & 0 & 4 \\ \cline{2-7}  & G-mean & 0 & 0 & 0 & 0 & 2 & 3 \\ \cline{2-7}  & F-measure & 0 & 0 & 1 & 0 & 0 & 4 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Haberman \\ (24.69\%) \\ \end{tabular} } & Precision & 0 & 0 & 0 & 2 & 0 & 3 \\ \cline{2-7}  & G-mean & 0 & 0 & 0 & 1 & 0 & 4 \\ \cline{2-7}  & F-measure & 0 & 0 & 0 & 1 & 0 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of classification and comparison of highly noisy and severely unbalanced data sets. data of industrial manufacturing processing. In this study, the oversampling technology is also used to process data samples, and part of the sampling process is modified in the process of use, forming a majority class synthesis minority class oversampling technology. (2) In the process of using industrial data information, the noise problem is a common problem, but when the noise problem is fused with unbalanced samples, the way of data pre-noising is easy to lose effective data instances, so it is necessary to propose targeted noise processing algorithms. (3) This study proposes a majority-class weighted minority-class oversampling technique (MWMOTE-FRIS-INFFC) based on the combination of fuzzy rough instances and classification fusion iterative filtering. In the process of inter-class identification, the majority-class-weighted minority-class oversampling technique is used to expand the data set to a certain extent, thus effectively solving the problem of unbalanced data. The Euclidean distance is used to assign sample weights so as to synthesize and insert new samples in the samples with large weights and a few classes so as to solve the problem of insufficient data clusters in the sample class of a few classes. Subsequently, fuzzy rough instance selection (FRIS) is used to remove some of the synthetic minority samples with low membership degree to the cluster, which can effectively solve the overfitting phenomenon of synthetic oversampling to the minority samples. Then, it is necessary to introduce the classification fusion iterative filter (INFFC) to solve the integrated noise problem, such as original noise and synthetic data noise, and then complete the data processing. (4) In this study, 8 data sets and 5 unbalanced and noisy sample classification models proposed by other scholars of the same type were used to carry out cross experiments, and the test was carried out under the condition of unified experimental conditions as far as possible. The test results show that the algorithm proposed in this study has the best effect.