## 6 Empirical Analysis
### The Test Analysis  
The number of neighbors selected by the KNN-SK and SKNN-SK is \(K=10\). The testing results of the (Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criteria)DE-MPFSC algorithm and other algorithms under the verification indicators are shown in Table 8 and Figures 16-18 about Classification Accuracy \((CA)\), Adjusted Rand Index \((ARI)\), and Normalized Mutual Information \((NMI)\) datasets. Where according to the data analysis in Table 8, we know that since the single data integrated method KNN-SK and SKNN-SK do not have fitting characteristics inside the data integration and consistency are better than single integrated methods. The CA value, ARI value and NMI value of UCI data about DE-MPFSC algorithm are as follows: the optimal value and sub-optimal value tend to distribution in probability 1, while the other data points tend to distribution in probability 0, in which the data points tended to distribution in probability 1 are in the feasible region and the data points tended to distribution in probability 0 are in the non-feasible region. The CA value, ARI value and NMI value of Credit Approval data about DE-MPFSC algorithm are as follows: the optimal value and sub-optimal value tend to distribution in probability 1, while the other data points tend to distribution in probability 0, in which the data points tended to distribution in probability 0 are in the non-feasible region. For the data (CMC) and Glass marked with \(\blacksquare\), the values of CA, ARI and NMI are as follows: the optimal value and the sub-optimal value are stable around the probability of 0.5, and the error does not exceed \(\pm\)0.2, which accord with the stability requirements of data. The CA value, ARI value and NMI value of all data about DE-MPFSC algorithm are as follows: the average optimal value and the average sub-optimal quality are stable around the probability of 0.5 and the error does not exceed \(\pm\)0.2. The variance optimal value and the variance sub-optimal value are basically stable around 0.01, which accord with the data stability standard and the error does not exceed 0.0001.From the datasets in Table 8, the optimal value of the DE-MPFSC algorithm in both mean and variance shows the superiority of the algorithm. It can be seen from the Figures 16-18 that through the two-dimensional reduced order analysis of UCI machine learning data, it is found that the three data integration effects of \(CA\), \(NMI\) and \(ARI\) show a trend of highly integrated convergence within an acceptable error range. First, we analyze from the precision. The integration effect on CA data is as follows: the minimum upper bound of the reduced-order 2D data in the \(X\) direction is \(\Omega_{min}=0.4\) and the error does not exceed \(\pm 0.01\). The maximum lower bound of the reduced-order 2D data in the \(Y\) direction is \(\Omega_{max}=0.1\) and the error does not exceed \(\pm 0.02\). The entanglement error of the imbalanced data set in both X and Y directions is \(\frac{\Omega_{max}}{\Omega_{min}}=0.25\). It can be seen that the precision in both directions of \(X\) and \(Y\) is in a fully controllable and highly integrated range, which fully shows that the DE-MPFSC algorithm has a good integration advantage for improving the integration accuracy of CA data. The integration effect on NMI data is as follows--the minimum upper bound of the reduced-order 2D data in the \(X\) direction is \(\Omega_{min}=0.3\) and the error does not exceed \(\pm 0.03\). The maximum lower bound of the reduced-order 2D data in the \(Y\) direction is \(\Omega_{max}=0.02\) and the error does not exceed \(\pm 0.01\). The entanglement error of the imbalanced data set in both \(X\) and \(Y\) directions is \(\frac{\Omega_{max}}{\Omega_{min}}=0.06\). It can be seen that the precision in both directions of \(X\) and \(Y\) is in a fully controllable and highly integrated range, which fully shows that the DE-MPFSC algorithm has a good integration advantage for improving the integration accuracy of ARI data.