Empirical comparison of techniques for handling incomplete data using decision trees

Abstract

Increasing the awareness of how incomplete data affects learning and classification accuracy has led to increasing numbers of missing data techniques. This paper investigates the robustness and accuracy of seven popular techniques for tolerating incomplete training and test data for different patters of missing data; different proportions and mechanisms of missing data on resulting tree-based models