2004 AligningBoundaryInKernelSpace

• (Wu & Chang, 2004) ⇒ Gang Wu, Edward Y. Chang. (2004). “Aligning Boundary in Kernel Space for Learning Imbalanced Dataset.” In: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM 2004) doi:10.1109/ICDM.2004.10106

Subject Headings: Imbalanced Supervised Classification Algorithm, Imbalanced Training Dataset.

Cited By

• Cited by ~11 http://scholar.google.com/scholar?cites=4221501712428465604

Quotes

Abstract

n [[Imbalanced Training Dataset|imbalanced training dataset]] poses serious problem for many real-world supervised learning tasks. In this paper, we propose a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy. Using a simple example, we first show that SVMs can suffer from high incidences of false negatives when the training instances of the target class are heavily outnumbered by the training instances of a non-target class. The remedy we propose is to adjust the class boundary by modifying the kernel matrix, according to the imbalanced data distribution. Through theoretical analysis backed by empirical study, we show that our kernel-boundary-alignment algorithm works effectively on several datasets.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 AligningBoundaryInKernelSpace	Gang Wu Edward Y. Chang			Aligning Boundary in Kernel Space for Learning Imbalanced Dataset			http://scholar.google.com/scholar?cluster=4221501712428465604	10.1109/ICDM.2004.10106