## 4 Algorithm Testing and Discussion
### Comparison of Algorithm Evaluation Results  
The precision summary results of different algorithms in different data sets are shown in Figure 1. The G-mean summary results of different data sets and different algorithms are shown in Figure 2. The summary results of the F-measure under different data sets and different algorithms are shown in Figure 3. The optimal value of the data result is displayed in bold font. (Because the algorithm classifier proposed in this paper chooses the combination of multiple classifiers and iterative filtering algorithms, it has obvious advantages over other algorithms [27; 28], so the subsequent classifiers of other algorithms all use the same neural network classifier, because, according to the research of other scholars, the neural network classifier has the best effect and is not affected by the filtering algorithm proposed in this study.).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**NO.** & **NAME** & **Minority Class** & **Majority Class** & \begin{tabular}{c} **Minority** \\ **Quantity** \\ \end{tabular} & \begin{tabular}{c} **Majority** \\ **Quantity** \\ \end{tabular} & \begin{tabular}{c} **Majority** \\ **Quantity** \\ \end{tabular} & \
\begin{tabular}{c} **Total Quantity** \\ \end{tabular} &
\begin{tabular}{c} **Imbalanced** \\ **Ratios** \\ \end{tabular} \\ \hline
1 & Wpbc & N & R & 47 & 151 & 198 & 1/3.23 \\
2 & Pima & 1 & 0 & 268 & 500 & 768 & 1/1.87 \\
3 & Yeast &
\begin{tabular}{c} ME3/MEW/EXC/VAC/POX/ERL \\ \end{tabular} & Other & 304 & 1180 & 1484 & 1/3.88 \\
4 & Liver & 1 & Other & 71 & 107 & 178 & 1/1.37 \\
5 & NEW-thyroid & 2 & Other & 35 & 180 & 215 & 1/5.15 \\
6 & DS1.1100 & 1 & 0 & 804 & 25,929 & 26,733 & 1/32.25 \\
7 & Abalone9-18 & 18 & 9 & 42 & 689 & 731 & 1/16.41 \\
8 & Iris & 2 & Other & 50 & 100 & 150 & 1/2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Unbalanced data and information summary.  
Figure 1: Precision values for different algorithms in different data sets.  
Figure 2: G-mean values for different algorithms in different data sets.  
According to the analysis in Figures 1-3, the algorithm proposed in this paper (MWMOTE-FRIS-INFFC) has the best comprehensive performance. For example, on most data sets, the classification accuracy of the algorithm proposed in this paper exceeds 75% without training a large number of data samples. The accuracy rate is relatively high. Some other algorithms also have some ability to deal with unbalanced samples and anti-noise. According to the analysis in Figure 2, the algorithm proposed in this paper still has the best overall effect. The optimal value of the algorithm proposed in this paper is generally higher. According to the analysis in Figure 3, in addition, under the classification performance of different data sets, the overall classification result of the proposed algorithm is relatively good, and the classification accuracy of the liver data set is the lowest, which is 39.94%. In addition, the classification performance of other data sets is also relatively good, among which the DS1.1100 data set shows the best classification results, and the classification accuracy is 85.57%. At the same time, the overall trend is similar in terms of the performance of G-mean and F-measure analysis [29; 30; 31]. The performance of the algorithm proposed in this study is relatively good, and the overall value fluctuation is relatively stable, which proves that the algorithm proposed in this study has similar working effects on different data sets and can complete the work of anti-noise and unbalanced sample classification.