# A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection
## 3 Results
However, PSOFI has a significant performance advantage over MOGI in most other cases. Moreover, the performance stability of PSOFI and DEKCF is better than MOGI, which is easy to infer from the figures. The F1 score box plots for all the datasets are illustrated in Figure 6. From the presented F1 score results, the superiority of evolutionary-algorithm-based methods' performance over statistics-based methods is once again highlighted. A detailed analysis comparing the proposed PSOFI method and DEKCF is presented below. On the Lymp dataset with 5%, 10%, 30%, and 50% MRs, the performance of PSOFI is slightly lower than that of DEKCF. DEKCF performs better than PSOFI on Spec, DBWS, and Aus datasets with six different MRs, and HV and GL datasets with 10%, 20%, 30%, 40%, and 50% MRs. Moreover, PSOFI outperforms DEKCF in other results, which are consistent with the earlier-mentioned accuracy results. When comparing MOGI and PSOFI, the results are quite similar to the accuracy results. PSOFI performs worse on the HV dataset with different MRs, BTSC and ILPD datasets with 10%, 20%, 30%, 40%, and 50% MRs, and Monk dataset with 20%, 30%, 40%, and 50% MRs. Additionally, PSOFI has lower performance on the Spec dataset with 40% and 50% MRs and Aus and GL datasets with a 50% MR. Nevertheless, PSOFI shows superior performance in other results compared to MOGI. Figure 4: Iteration curves of PSOFI and PSOFIwl on three datasets with different MRs: (**a**) with 5% MR; (**b**) with 10% MR; (**c**) with 20% MR; (**d**) with 30% MR; (**e**) with 40% MR; (**f**) with 50% MR.  
Now, the performance of PSOIM and PSOFS is analyzed to see how the imputation and feature selection techniques influence PSOFI. In the figures of accuracy and F1 score results, PSOIM and PSOFS have better performance in almost every case when compared with statistics-based methods. When it comes to the evolutionary-algorithm-based imputation methods, PSOFI outperforms PSOIM and PSOFS in almost all cases, and DEKCF is only slightly inferior to PSOIM or PSOFS in a few cases. PSOFS is inferior to MOGI on high-dimensional datasets but it is competitive with MOGI on other datasets with MRs lower than 0.0000. Figure 5: Accuracy box plot results on datasets with different MRs, where different colors represent different MRs: (**a**) on BCC dataset; (**b**) on Park dataset; (**c**) on BTSC dataset; (**d**) on HV dataset; (**e**) on Lymp dataset; (**f**) on Spec dataset; (**g**) on Monk dataset; (**h**) on DBWS dataset; (**i**) on Sta dataset; (**j**) on ILPD dataset; (**k**) on Aus dataset; (**l**) on GL dataset; (**m**) figure legend. than 30%. MOGI is superior to PSOIM in all cases except for the DBWS dataset. PSOFS has a weaker performance than PSOIM on BTSC, DBWS, and GL datasets with different MRs, the HV dataset with MRs higher than 5%, and Park, Lymp, Spec, Monk, and ILPD datasets with MRs higher than 20%. To summarize, PSOIM is effective in imputation but the performance is relatively low. PSOFS can handle datasets with low MRs to a certain extent but, as the MR increases, its performance declines quickly. PSOFS cannot handle high-dimensional datasets well. It may encounter situations where the classifier fails to operate normally on the dataset, especially when the dataset is high-dimensional, as there are still some missing values in the selected features, resulting in no outcomes. It is also worth noting that the box plot results do not include these cases. As PSOFI has significantly better performance than both PSOIM and PSOFS, it can be inferred that integrating feature selection techniques in imputation methods can be a great help in improving the overall classification performance. To statistically confirm the superiority of PSOFI, the Mann-Whitney U test is conducted for verification.