Similarity-learning information-fusion schemes for missing data imputation

Abstract

Missing data imputation is a very important data cleaning task for machine learning and data mining with incomplete data. This paper proposes two novel methods for missing data imputation, named kEMI and kEMI+, that are based on the k-Nearest Neighbours algorithm for pre-imputation and the Expectation–Maximization algorithm for posterior-imputation. The former is a local search mechanism that aims to automatically find the best value for k and the latter makes use of the best k nearest neighbours to estimate missing scores by learning global similarities. kEMI+ makes use of a novel information fusion mechanism. It fuses top estimations through the Dempster–Shafer fusion module to obtain the final estimation. They handle both numerical and categorical features. The performance of the proposed imputation techniques are evaluated by applying them on twenty one publicly available datasets with different missingness and ratios, and, then, compared with other state-of-the-art missing data imputation techniques in terms of standard evaluation measures such as the normalized root mean square difference and the absolute error. The attained results indicate the effectiveness of the proposed novel missing data imputation techniques.