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dc.contributor.author Macropol, Kathy en
dc.date.accessioned 2009-05-20T18:13:01Z en
dc.date.available 2009-05-20T18:13:01Z en
dc.date.copyright 2007 en
dc.date.issued 2007-05 en
dc.identifier.uri http://hdl.handle.net/10139/626 en
dc.description.abstract The digital revolution has brought an explosion of stored data to our world, and data mining, especially on the rapidly expanding medical databases, has the capabilities to turn this information into new and useful medical knowledge. Data mining with cost sensitive decision trees created using Genetic Programming is focused upon, with an emphasis on their relevence and potential in medical data mining. An application that uses elitist multiobjective Genetic Programming to obtain a pareto front of univariate and linear decision trees was implemented, and various mutation operators were tested and compared on their performance in the development of the decision trees. Acknowledgements en
dc.language.iso en_US en
dc.rights All rights reserved to author and California State University Channel Islands en
dc.subject Data mining en
dc.subject Medical databases en
dc.subject Computer Science thesis en
dc.title Genetic Programming and Decision Trees Applied to Medical Data Mining en
dc.type Thesis en

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