An Incremental Algorithm for Repairing Training Sets with Missing Values
Date:
Real-life datasets that occur in domains such as industrial process control, medical diagnosis, marketing, risk management, often contain missing values. This poses a challenge for many classification and regression algorithms which require complete training sets. In this paper we present a new approach for repairing such incomplete datasets by constructing a sequence of regression models that iteratively replace all missing values. Additionally, our approach uses the target attribute to estimate the values of missing data. The accuracy of our method, Incremental Attribute Regression Imputation, IARI, is compared with the accuracy of several popular and state of the art imputation methods, by applying them to five publicly available benchmark datasets. The results demonstrate the superiority of our approach.