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Title: SNEOM: a sanger network based extended over-sampling method. Application to imbalanced biomedical datasets
Authors: Martínez-García, José Manuel
Suárez-Araujo, Carmen Paz 
Báez, Patricio García
UNESCO Clasification: 120304 Inteligencia artificial
32 Ciencias médicas
Keywords: Neural-Networks
Issue Date: 2012
Publisher: 0302-9743
Journal: Lecture Notes in Computer Science 
Conference: 19th International Conference on Neural Information Processing (ICONIP) 
19th International Conference on Neural Information Processing, ICONIP 2012 
Abstract: In this work we introduce a novel over-sampling method to face the problem of imbalanced classes’ classification. This method, based on the Sanger neural network, is capable of dealing with high-dimensional datasets. Moreover, it extends the capability of over-sampling methods and allows generating samples from both minority and majority classes. We have validated it in real medical applications where the involved datasets present an un-even representation among the classes and it has been obtained high sensitivities identifying minority classes. Therefore, by means of this method it is possible to accomplish the design of systems for the medical diagnosis with a high reliability.
ISBN: 9783642344770
ISSN: 0302-9743
DOI: 10.1007/978-3-642-34478-7_71
Source: Huang T., Zeng Z., Li C., Leung C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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