Synthetic over-sampling in the empirical feature space

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Áreas de investigación:
Año:
2013
Tipo de publicación:
Artículo en conferencia
Autores:
Título del libro:
21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2013)
Páginas:
385-390
Dirección:
Brudge
Organización:
Bruges, Belgium
Mes:
24th-25th April
ISBN:
978-2-87419-081-0
BibTex:
Abstract:
The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to different approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with the kernel classifiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space) to develop a kernel-based synthetic over-sampling technique, which maintains the main properties of the kernel mapping. The proposal achieves better results than the same oversampling method applied to the original input space.
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