NIIA: Nonparametric Iterative Imputation Algorithm

Abstract

Many missing data imputation methods are based on only complete instances (instances without missing values in a dataset) when estimating plausible values for the missing values in the dataset. Actually, the information within incomplete instances (instances with missing values) can also play an important role in missing value imputation. For example, the information has been applied to identifying the neighbors of an instance with missing values in NN (nearest neighbor) imputation, and the class of the instance in clustering-based imputation, where NN and clustering-based imputations are well-known efficient algorithms. Therefore, in this paper we advocate to well utilize the information within incomplete instances when estimating missing values. As an attempt, a simple and efficient nonparametric iterative imputation algorithm, called NIIA method, is designed for imputing iteratively missing target values. The NIIA method imputes each missing value several times until the algorithm converges. In the first iteration, all complete instances are used to estimate missing values. The information within incomplete instances is utilized since the second iteration. We conduct intensive experiments for evaluating the proposed approach. Our experimental results show: (1) The utilization of information within incomplete instances is of benefit to capture the distribution of a dataset much better and easier than parametric imputation. (2) NIIA method outperforms the existing methods at the accuracy, and this advantage is clearly highlighted when datasets are with high missing ratio.