图 书 馆
文本分类中的有效多标签主动学习Effective Multi-Label Active Learning for Text Classification |
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| 课程网址: | http://videolectures.net/kdd09_yang_emlaltc/ |
| 主讲教师: | Bishan Yang |
| 开课单位: | 北京大学 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling efforts, we propose a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. Our approach takes into account the multi-label information, and select the unlabeled data which can lead to the largest reduction of the expected model loss. Specifically, the model loss is approximated by the size of version space, and the reduction rate of the size of version space is optimized with Support Vector Machines (SVM). An effective label prediction method is designed to predict possible labels for each unlabeled data point, and the expected loss for multi-label data is approximated by summing up losses on all labels according to the most confident result of label prediction. Experiments on several real-world data sets (all are publicly available) demonstrate that our approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods. |
| 关 键 词: | 文本数据; 主动学习方法; 分类精度; 支持向量机 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-29：nkq |
| 阅读次数: | 121 |