Adaptive feature-space conformal transformation for imbalanced-data learning
Published 21 August 2003
Gang Wu, Edward Yi Chang
Citations99
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TL;DR
Experimental results on UCI and real-world datasets show an adaptive conformal transformation algorithm, ACT, to be effective in improving class prediction accuracy.
Abstract
When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determin-ing the class boundary. To remedy this problem, we propose an adaptive conformal transformation (ACT) algorithm. ACT considers feature-space distance and the class-imbalance ratio when it per-forms conformal transformation on a kernel func-tion. Experimental results on UCI and real-world datasets show ACT to be effective in improving class prediction accuracy. 1.
Keywords
Computer Science
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