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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 391 - 437 (47page)
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Machine learning has progressed to match human performance, including the field of text classification. However, when training data are imbalanced, classifiers do not perform well. Oversampling is one way to overcome the problem of imbalanced data and there are many oversampling methods that can be conveniently implemented. While comparative researches of oversampling methods on non-text data have been conducted, studies comparing oversampling methods under a unifying framework on text data are scarce. This study finds that while oversampling methods generally improve the performance of classifiers, similarity is an important factor that influences the performance of classifiers on imbalanced and resampled data.
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참고문헌 (27)
참고문헌 신청
Aggarwal, C. C. / 2013 / Mining Text Data / Springer US : 163 ~ 222
Ailab / 2011 / Evaluation: from Precision, Recall and F-measure to Roc, Informedness, Markedness & Correlation / Journal of Machine Learning Technologies 2 (1) : 37 ~ 63
Aridas, C. K. / 2016 / Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning / Computing Research Repository, abs/1609.06570
Bai, Y. / 2008 / ADASYN : Adaptive synthetic sampling approach for imbalanced learning / 2008 IEEE International Joint Conference on Neural Networks(IEEE World Congress on Computat
Barua S. / 2011 / Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063 / Springer
Ailab / 2011 / Evaluation: from Precision, Recall and F-measure to Roc, Informedness, Markedness & Correlation / Journal of Machine Learning Technologies 2 (1) : 37 ~ 63
Aridas, C. K. / 2016 / Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning / Computing Research Repository, abs/1609.06570
Bai, Y. / 2008 / ADASYN : Adaptive synthetic sampling approach for imbalanced learning / 2008 IEEE International Joint Conference on Neural Networks(IEEE World Congress on Computat
Barua S. / 2011 / Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063 / Springer
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