Trinity College Dublin, Department of Computer Science
Tsymbal, Alexey. 'Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2006-25, 2006, pp6
Computer Science Technical Report TCD-CS-2006-25
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.
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