图 书 馆
与PCT的多标签分类的距离比较Comparison of distances for multi-label classification with PCTs |
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| 课程网址: | http://videolectures.net/sikdd2011_gjorgjioski_classification/ |
| 主讲教师: | Valentin Gjorgjioski |
| 开课单位: | 约瑟夫·斯特凡学院 |
| 开课时间: | 2011-11-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multi-label classification has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label classification methods. These methods either transform the multi-label dataset to several simpler datasets or adapt the learning algorithm so it can handle the multiple labels. In this paper, we consider the latter approach. Namely, we use predictive clustering trees to perform multi-label classification. Furthermore, we perform an experimental comparison of four distance measures used to select the splits in the nodes of the trees. The experimental evaluation was conducted on 6 benchmark datasets using 6 different evaluation measures. The results show that, averaged overall, the Euclidean distance and the Hamming loss yield the best predictive performance. |
| 关 键 词: | 计算机科学; 机器学习; 多任务学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-28：yumf |
| 阅读次数: | 14 |