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Empirical comparison of techniques for handling incomplete data using decision trees

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dc.contributor.author Twala, B
dc.date.accessioned 2009-05-12T11:17:47Z
dc.date.available 2009-05-12T11:17:47Z
dc.date.issued 2009
dc.identifier.citation Twala, B. 2009. Empirical comparison of techniques for handling incomplete data using decision trees. Applied Artificial Intelligence, pp 1-35 en
dc.identifier.issn 0883-9514
dc.identifier.uri http://hdl.handle.net/10204/3373
dc.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 en
dc.description.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 en
dc.language.iso en en
dc.publisher Taylor & Francis en
dc.subject Data handling techniques en
dc.subject Incomplete data handling en
dc.subject Missing data techniques en
dc.subject MDTs en
dc.subject Tree-based models en
dc.subject Machine learning en
dc.subject Classification accuracy en
dc.subject Digital intelligence en
dc.title Empirical comparison of techniques for handling incomplete data using decision trees en
dc.type Article en
dc.identifier.apacitation Twala, B. (2009). Empirical comparison of techniques for handling incomplete data using decision trees. http://hdl.handle.net/10204/3373 en_ZA
dc.identifier.chicagocitation Twala, B "Empirical comparison of techniques for handling incomplete data using decision trees." (2009) http://hdl.handle.net/10204/3373 en_ZA
dc.identifier.vancouvercitation Twala B. Empirical comparison of techniques for handling incomplete data using decision trees. 2009; http://hdl.handle.net/10204/3373. en_ZA
dc.identifier.ris TY - Article AU - Twala, B AB - 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 DA - 2009 DB - ResearchSpace DP - CSIR KW - Data handling techniques KW - Incomplete data handling KW - Missing data techniques KW - MDTs KW - Tree-based models KW - Machine learning KW - Classification accuracy KW - Digital intelligence LK - https://researchspace.csir.co.za PY - 2009 SM - 0883-9514 T1 - Empirical comparison of techniques for handling incomplete data using decision trees TI - Empirical comparison of techniques for handling incomplete data using decision trees UR - http://hdl.handle.net/10204/3373 ER - en_ZA


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