图 书 馆
处理不平衡回归任务的重采样方法Resampling Approaches for Handling Imbalanced Regression Tasks |
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| 课程网址: | https://videolectures.net/videos/solomon_torgo_regression_tasks |
| 主讲教师: | Luis Torgo |
| 开课单位: | 所罗门研讨会 |
| 开课时间: | 2017-01-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Imbalanced classification tasks have been studied by the research community for a long time. Numerous problems have been identified with standard approaches and new proposals have been put forward for addressing these relevant tasks. Surprisingly, the same attention has not been given to predictive tasks with a numeric target variable, i.e. regression. However, similar problems occur on these domains, when the target of the end-user is the performance on a subset of rare values of the target variable. As in classification standard evaluation metrics fail, and new approaches are required to bias the learning algorithms to the end-user goals. In this talk we will present resampling approaches to these problems. These methods have as main advantage the possibility of being used together with any existing regression tool and still focus on the goals of the end-user, i.e. values poorly represented in the training data. |
| 关 键 词: | 不平衡回归任务; 重采样法; 分类标准评估 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-12-27：liyq |
| 最后编审: | 2024-12-27：liyq |
| 阅读次数: | 12 |