Effectiveness of Correlation Assisted SVM-based Imputation Method for Missing Data Prediction

Handling missing data is a critical challenge in data processing, as it can significantly impact the performance of machine learning models. This paper proposes a Correlation Assisted Support Vector Machine (CA-SVM) based imputation method that integrates correlation analysis with the predictive power of Support Vector Machines to enhance missing data prediction accuracy. The CA-SVM approach identifies highly correlated attributes to guide the SVM in estimating missing values more effectively, preserving the underlying structure of the dataset. A comparative study is conducted between the proposed CA-SVM, standard SVM, and K-Nearest Neighbors (KNN) imputation techniques using benchmark datasets. Experimental results demonstrate that the CA-SVM method achieves a prediction accuracy of approximately 97 per cent, outperforming both traditional SVM and KNN methods in terms of accuracy, robustness, and consistency. The findings confirm that correlation-based feature guidance significantly improves the imputation performance of SVM models. This research highlights the potential of hybrid imputation techniques in building more reliable and accurate data-driven systems.