Local ensemble learning from imbalanced and noisy data for word sense disambiguation
作者：
Highlights：
• A study of effects of class imbalance and label noise on word sense disambiguation.
• Local ensemble learning robust to class skewness and corrupted training labels.
• Random subspaces and kernel whitening for handling high-dimensional data.
• Two-level fusion for efficient usage of one-class classifiers for multi-class tasks.
• Thorough experimental study on challenging real-life word sense disambiguation data.
摘要
•A study of effects of class imbalance and label noise on word sense disambiguation.•Local ensemble learning robust to class skewness and corrupted training labels.•Random subspaces and kernel whitening for handling high-dimensional data.•Two-level fusion for efficient usage of one-class classifiers for multi-class tasks.•Thorough experimental study on challenging real-life word sense disambiguation data.
论文关键词：Machine learning,Natural language processing,Imbalanced classification,Multi-class imbalance,Ensemble learning,One-class classification,Class label noise,Word sense disambiguation
论文评审过程：Received 18 April 2017, Revised 11 October 2017, Accepted 21 October 2017, Available online 23 October 2017, Version of Record 3 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.028
