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Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
Tsymbal, Alexey
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.
Keyword(s): Computer Science
Publication Date:
2006
Type: Report
Peer-Reviewed: Unknown
Language(s): English
Institution: Trinity College Dublin
Funder(s): Science Foundation Ireland
Citation(s): 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
Publisher(s): Trinity College Dublin, Department of Computer Science
File Format(s): application/pdf
First Indexed: 2014-05-13 05:10:10 Last Updated: 2015-04-10 05:13:44