Effect of Missing Data Treatment on the Predictive Accuracy of C4.5 Classifier

Missing data is a common problem confronted by researchers in machine learning applications. Missing values affect both the performance of analysis tools, as well as the quality of the drawn decisions. This research aims to analyze the impact of four missing data treatment methods on the predictive accuracy of the C4.5 decision tree algorithm. It also investigates the imputation accuracy of each imputation method using a single dataset with...