Synthetic over-sampling in the empirical feature space

Research areas:

• Methodology - Machine learning

Year:

2013

Type of Publication:

In Proceedings

Authors:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Book title:

21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2013)

Pages:

385-390

Address:

Brudge

Organization:

Bruges, Belgium

Month:

24th-25th April

ISBN:

978-2-87419-081-0

BibTex:

@conference{SOEFS2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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. ", address = "Brudge", booktitle = "21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2013)", doi = "http://www.i6doc.com/en/livre/?GCOI=28001100131010", isbn = "978-2-87419-081-0", month = "24th-25th April", organization = "Bruges, Belgium", pages = "385-390", title = "{S}ynthetic over-sampling in the empirical feature space", url = "https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-103.pdf", year = "2013", }

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.