SNEOM: a sanger network based extended over-sampling method. application to imbalanced biomedical datasets

Authors:
José Manuel Martínez-García;Carmen Paz Suárez-Araujo;Patricio García Báez
Affiliations:
Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Canary Islands, Spain;Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Canary Islands, Spain;Departamento de Estadística, Investigación Operativa y Computación, Universidad de La Laguna, La Laguna, Canary Islands, Spain
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

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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.