###### Abstract  
Data are crucial components of machine learning and deep learning in real-world applications. However, when collecting data from actual systems, we often encounter issues with missing information, which can harm accuracy and lead to biased results. In the context of video surveillance, missing data may arise due to obstructions, varying camera angles, or technical issues, resulting in incomplete information about the observed scene. This paper introduces a method for handling missing data in tabular formats, specifically focusing on video surveillance. The core idea is to fill in the missing values for a specific feature using values from other related features rather than relying on all available features, which we refer to as the imputation approach based on informative features. The paper presents three sets of experiments. The first set uses synthetic datasets to compare four optimization algorithms--Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and the Sine-Cosine Algorithm (SCA)--to determine which one best identifies features related to the target feature. The second set works with real-world datasets, while the third focuses on video-surveillance datasets. Each experiment compares the proposed method, utilizing the best optimizer from the first set, against leading imputation methods. The experiments evaluate different types of data and various missing-data rates, ensuring that randomness does not introduce bias. In the first experiment, using only synthetic data, the results indicate that the WOA-based approach outperforms PSO, GWO, and SCA optimization algorithms. The second experiment used real datasets, while the third used tabular data extracted from a video-surveillance system. Both experiments show that our WOA-based imputation method produces promising results, outperforming other state-of-the-art imputation methods. m 202510 January  
Missing data can introduce bias, reduce statistical power, and lead to incorrect conclusions if not handled properly. Ignoring missing data or simply removing incomplete cases can result in biased estimates and loss of valuable information. This limitation underscores the importance of using appropriate techniques to effectively address missing data during the preprocessing phase [3; 4; 5]. Imputation methods are techniques for estimating missing values based on the information available in the dataset. The aim of these methods is to fill in the missing values with plausible substitutes while preserving the statistical properties of the data. Imputation allows researchers to retain more observations. It also preserves the integrity of the dataset for analysis [6; 7; 8; 9]. There are several types of imputation methods, both single and multiple. For example, the mean, median, or mode imputation method replaces missing values with the mean, median, or mode of the observations in the variable [10]. The hot deck imputation method uses values from similar cases or nearest neighbors to impute missing values [11], while the cold deck imputation method uses values from external data sources or historical records to impute missing values [12]. Regression imputation could also be used, where missing values are predicted by regression models based on other variables in the dataset [4]. K-Nearest Neighbor (KNN) imputation deals with missing values by taking into account the local structure of the data. It uses the mean or mode of the \(K\) of neighbors to estimate missing values [13]. In addition, Generative Adversarial Imputation Networks (GAIN) is an innovative imputation technique that harnesses the power of deep learning, and Generative Adversarial Networks (GANs) to impute missing values in datasets [14]. In addition, multiple imputation imputes multiple sets of plausible values for each missing value and perform analyses on each imputed dataset to produce a combined set of results [15].