By Topic

KBA: kernel boundary alignment considering imbalanced data distribution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
G. Wu ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA ; E. Y. Chang

An imbalanced training data set can pose serious problems for many real-world data mining tasks that employ SVMs to conduct supervised learning. In this paper, we propose a kernel-boundary-alignment algorithm, which considers THE 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 nontarget 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 data sets.

Published in:

IEEE Transactions on Knowledge and Data Engineering  (Volume:17 ,  Issue: 6 )