## 5 Conclusions and Future Work  
Data are critical foundations for machine learning, both for model training and validation. However, datasets collected from real-world systems often suffer from missing-data issues, which prevent these valuable resources from being fully utilized. The phenomenon of missing data is prevalent in many applications, especially in video surveillance, and it greatly affects the accuracy and reliability of evaluations. Therefore, imputing missing values is crucial for ensuring reliable analyses and avoiding bias. Researchers in the field \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Single** & **KNN** & **MICE** & **GAIN** & **FS-WOA** \\ \hline \multicolumn{6}{c}{**Missing rate 5\%**} \\ \hline VSD\({}_{1}\) & 0.0493 (4.0) & 0.0348 (3.0) & **0.0233 (1.0)** & 0.0500 (5.0) & 0.0250 (2.0) \\ VSD\({}_{2}\) & 0.0480 (5.0) & 0.0124 (3.0) & 0.0069 (2.0) & 0.0228 (4.0) & **0.0059 (1.0)** \\ VSD\({}_{3}\) & 0.0516 (5.0) & 0.0170 (4.0) & 0.0067 (2.0) & 0.0162 (3.0) & **0.0055 (1.0)** \\ VSD\({}_{4}\) & 0.0524 (5.0) & 0.0197 (3.0) & 0.0137 (2.0) & 0.0346 (4.0) & **0.0117 (1.0)** \\ VSD\({}_{5}\) & 0.0541 (5.0) & 0.0312 (3.0) & 0.0206 (2.0) & 0.0344 (4.0) & **0.0197 (1.0)** \\ \hline Average ranks & 4.8 & 3.2 & 1.8 & 4.0 & **1.2** \\ \hline \multicolumn{6}{c}{**Missing rate 15\%**} \\ \hline VSD\({}_{1}\) & 0.1402 (5.0) & 0.1330 (4.0) & 0.0722 (2.0) & 0.0950 (3.0) & **0.0648 (1.0)** \\ VSD\({}_{2}\) & 0.1358 (5.0) & 0.0574 (4.0) & 0.0262 (2.0) & 0.0540 (3.0) & **0.0233 (1.0)** \\ VSD\({}_{3}\) & 0.1424 (5.0) & 0.0839 (4.0) & 0.0235 (2.0) & 0.0348 (3.0) & **0.0232 (1.0)** \\ VSD\({}_{4}\) & 0.1476 (5.0) & 0.0790 (4.0) & 0.0427 (2.0) & 0.0596 (3.0) & **0.0378 (1.0)** \\ VSD\({}_{5}\) & 0.1504 (5.0) & 0.0974 (4.0) & 0.0679 (2.0) & 0.0846 (3.0) & **0.0593 (1.0)** \\ \hline Average ranks & 5.0 & 4.0 & 2.0 & 3.0 & **1.0** \\ \hline \multicolumn{6}{c}{**Missing rate 25\%**} \\ \hline VSD\({}_{1}\) & 0.2200 (4.0) & 0.2435 (5.0) & 0.1262 (2.0) & 0.1386 (3.0) & **0.1254 (1.0)** \\ VSD\({}_{2}\) & 0.2085 (5.0) & 0.1001 (4.0) & 0.0399 (2.0) & 0.0758 (3.0) & **0.0345 (1.0)** \\ VSD\({}_{3}\) & 0.2158 (5.0) & 0.1684 (4.0) & 0.0438 (2.0) & 0.0600 (3.0) & **0.0436 (1.0)** \\ VSD\({}_{4}\) & 0.2277 (5.0) & 0.1427 (4.0) & 0.0766 (2.0) & 0.0938 (3.0) & **0.0719 (1.0)** \\ VSD\({}_{5}\) & 0.2402 (5.0) & 0.1807 (4.0) & 0.1107 (2.0) & 0.1127 (3.0) & **0.1030 (1.0)** \\ \hline Average ranks & 4.8 & 4.2 & 2.0 & 3.0 & **1.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The results of different imputation approaches applied to video-surveillance datasets with different rates of missing data (in the form of MSE \((rnk)\)). have developed a variety of imputation methods; however, none of these methods consider the potential correlations between dataset features. The proposed imputation approach uses the features correlated with the target feature during the imputation process and ignores the remaining features. Features with more missing values are treated first to make the best use of the actual measurements. The imputation process uses an optimization algorithm to select the relevant features for each target feature to be imputed. This paper proposes a comparative study of the use of different optimization algorithms (PSO, SCA, WOA, GWO) in the imputation approach based on informative features. Then, the best of the compared algorithms (FS-WOA, as a result of the first set of experiments on synthetic data) is compared with state-of-the-art imputation algorithms on real datasets and video-surveillance datasets. The relevant feature data are used to train a random forest regression model, and the trained model is then used to impute the missing values. The experiments included synthetic, real-world, and video-surveillance dataset types with different missing rates. Although the first set of experiments on synthetic datasets shows no significant superiority of any of the compared optimization algorithms, WOA is relatively superior compared to other optimizations.