19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
Salt Lake City, Utah
June 22-June 23
ISBN: 0-7695-2517-1
DOI Bookmark:
http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.65
Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality of data and on the appropriate selection of a learning algorithm for the data.
In this paper we analyze the effect of class noise on supervised learning in medical domains. We review the related work on learning from noisy data and propose to use feature extraction as a pre-processing step to diminish the effect of class noise on the learning process. Our experiments with 8 medical datasets show that feature extraction indeed helps to deal with class noise. It clearly results in higher classification accuracy of learnt models without the separate explicit elimination of noisy instances.
Citation:
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, Oleksandr Pechenizkiy, "Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction," cbms, pp.708-713, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
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