Borderline kernel based over-sampling
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Autor
Pérez-Ortiz, María
Gutiérrez, Pedro A.
Hervás-Martínez, César
Fecha
2017-03-30Materia
Pattern recognitionKernel function
Empirical feature space
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Nowadays, the imbalanced nature of some real-world data
is receiving a lot of attention from the pattern recognition and machine
learning communities in both theoretical and practical aspects, giving
rise to di erent promising approaches to handling it. However, preprocessing
methods operate in the original input space, presenting distortions
when combined with kernel classi ers, that operate in the feature
space induced by a kernel function. This paper explores the notion of
empirical feature space (a Euclidean space which is isomorphic to the feature
space and therefore preserves its structure) to derive a kernel-based
synthetic over-sampling technique based on borderline instances which
are considered as crucial for establishing the decision boundary. Therefore,
the proposed methodology would maintain the main properties of
the kernel mapping while reinforcing the decision boundaries induced by
a kernel machine. The results show that the proposed method achieves
better results than the same borderline over- sampling method applied
in the original input space