Analysis of Machine Learning Based Imputation of Missing Data

Abstract

Data analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion- or imputation-based methods are used that result in the reduction of data records or imputation of incorrect predicted value. Quality of imputed data can be significantly improved if missing values are generated accurately using machine learning algorithms. In this work, an analysis of machine learning-based algorithms for missing data imputation is performed. The K-nearest neighbors (KNN) and Sequential KNN (SKNN) algorithms are used to impute missing values in datasets using machine learning. Missing values handled using a statistical deletion approach (List-wise Deletion (LD)) and ML-based imputation methods (KNN and SKNN) are then tested and compared using different ML classifiers (Support Vector Machine and Decision Tree) to evaluate the effectiveness of imputed data. The used algorithms are compared in terms of accuracy, and results yielded that the ML-based imputation method (SKNN) outperforms the LD-based approach and KNN method in terms of the effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT).