Abstract
Standard data analysis techniques for biomedical problems cannot take into account existing prior knowledge, and available literature results cannot be incorporated in further studies. In this work we review some techniques that incorporate prior knowledge in supervised classification algorithms as constraints to the underlying optimization and linear algebra problems. We analyze a case study, to show the advantage of such techniques in terms of prediction accuracy.
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Abbate, D., De Asmundis, R., Guarracino, M.R. (2010). Prior Knowledge in the Classification of Biomedical Data. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_1
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DOI: https://doi.org/10.1007/978-3-642-14746-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14745-6
Online ISBN: 978-3-642-14746-3
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