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» The Foundations of Cost-Sensitive Learning
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ICDM
2003
IEEE
134views Data Mining» more  ICDM 2003»
15 years 9 months ago
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conve...
Bianca Zadrozny, John Langford, Naoki Abe
ECML
2007
Springer
15 years 10 months ago
Roulette Sampling for Cost-Sensitive Learning
In this paper, we propose a new and general preprocessor algorithm, called CSRoulette, which converts any cost-insensitive classification algorithms into cost-sensitive ones. CSRou...
Victor S. Sheng, Charles X. Ling
CVPR
2010
IEEE
15 years 2 months ago
Cost-Sensitive Subspace Learning for Face Recognition
Conventional subspace learning-based face recognition aims to attain low recognition errors and assumes same loss from all misclassifications. In many real-world face recognition...
Jiwen Lu, Tan Yap-Peng
DMIN
2007
186views Data Mining» more  DMIN 2007»
15 years 5 months ago
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?
- The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurr...
Gary M. Weiss, Kate McCarthy, Bibi Zabar
154
Voted
COLING
2010
14 years 11 months ago
A Comparison of Models for Cost-Sensitive Active Learning
Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ann...
Katrin Tomanek, Udo Hahn