2011年11月
Improving Classifier Performance Using Data with Different Taxonomies
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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- 巻
- 23
- 号
- 11
- 開始ページ
- 1668
- 終了ページ
- 1677
- 記述言語
- 英語
- 掲載種別
- DOI
- 10.1109/TKDE.2010.170
- 出版者・発行元
- IEEE COMPUTER SOC
We propose a framework for improving classifier performance by effectively using auxiliary samples. The auxiliary samples are labeled not in terms of the target taxonomy according to which we wish to classify samples, but according to classification schemes or taxonomies that are different from the target taxonomy. Our method finds a classifier by minimizing a weighted error over the target and auxiliary samples. The weights are defined so that the weighted error approximates the expected error when samples are classified into the target taxonomy. Experiments using synthetic and text data show that our method significantly improves the classifier performance in most cases compared to conventional data augmentation methods.
- リンク情報
- ID情報
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- DOI : 10.1109/TKDE.2010.170
- ISSN : 1041-4347
- Web of Science ID : WOS:000295180500005