Abstract
This paper analyzes two different approaches to the detection of supporting factors used in descriptive induction. The first is based on the statistical comparison of the pattern properties relative to the properties of the entire negative and the entire positive example sets. The other approach uses artificially generated random examples that are added into the original training set. The methodology is illustrated in the analysis of patients suffering from brain ischaemia.
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References
Bay, S.D., Pazzani, M.J.: Detecting group differences: Mining contrast sets. Data Min. Knowl. Discov. 5(3), 213–246 (2001)
Friedman, J.H., Fisher, N.I.: Bump-hunting for high dimensional data. Statisics and Computing 9, 123–143 (1999)
Gamberger, D., Lavrač, N., Krstačić, G.: Active subgroup mining: A case study in a coronary heart disease risk group detection. Artificial Intelligence in Medicine 28, 27–57 (2003)
Gamberger, D., Lavrač, N., Krstačić, A., Krstačić, G.: Clinical data analysis based on iterative subgroup discovery: Experiments in brain ischaemia data analysis. Applied Intelligence (in press, 2007)
Lowry, R.: Concepts and Applications of Inferential Statistics (2007), http://faculty.vassar.edu/lowry/webtext.html
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© 2007 Springer-Verlag Berlin Heidelberg
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Gamberger, D., Lavrač, N. (2007). Supporting Factors in Descriptive Analysis of Brain Ischaemia. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_18
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DOI: https://doi.org/10.1007/978-3-540-73599-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
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