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
Reference:
Twala, B. 2009. Empirical comparison of techniques for handling incomplete data using decision trees. Applied Artificial Intelligence, pp 1-35
Twala, B. (2009). Empirical comparison of techniques for handling incomplete data using decision trees. http://hdl.handle.net/10204/3373
Twala, B "Empirical comparison of techniques for handling incomplete data using decision trees." (2009) http://hdl.handle.net/10204/3373
Twala B. Empirical comparison of techniques for handling incomplete data using decision trees. 2009; http://hdl.handle.net/10204/3373.
Author Posting. Copyright Taylor & Francis, 2009. This is the author's version of the work. It is posted here by permission of Taylor & Francis for personal use, not for redistribution
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