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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3373

Title: Empirical comparison of techniques for handling incomplete data using decision trees
Authors: Twala, B
Keywords: Data handling techniques
Incomplete data handling
Missing data techniques
MDTs
Tree-based models
Machine learning
Classification accuracy
Digital intelligence
Issue Date: 2009
Publisher: Taylor & Francis
Citation: Twala, B. 2009. Empirical comparison of techniques for handling incomplete data using decision trees. Applied Artificial Intelligence, pp 1-35
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
Description: 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
URI: http://hdl.handle.net/10204/3373
ISSN: 0883-9514
Appears in Collections:Digital intelligence
General science, engineering & technology

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