Examination of Reliability of Missing Value Recovery in Data Mining

Missing data imputation is an important task in cases where it is crucial to use all available data and no discard records with missing values. However, most of the existing algorithms are focused on missing at random (MAR) or missing completely at random (MCAR). In this paper, an information decomposition imputation (IDIM) algorithm using fuzzy membership function is proposed for addressing the missing value problem which is not missing at random (NMAR). The reliability of missing value recovery at not missing at random is examined. Firstly, this paper will discuss the proposed IDIM algorithm with detailed examples. Then, the reliability of the proposed approach is evaluated with extensive experiments compared with some typical algorithms, and the results demonstrate that the proposed algorithm has a higher accuracy rate than the exiting imputation methods in terms of normal root mean square error (NRMSE) and predictive accuracy at different set of data with missing values, which shows our method is more reliable in imputing missing values.