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使用多重包含准则（MIC）选择多任务特征Multi-Task Feature Selection Using the Multiple Inclusion Criterion (MIC) |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_talukdar_mtfsumic/ |
| 主讲教师: | Partha Pratim Talukdar |
| 开课单位: | 印度班加罗尔理工学院 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We address the problem of joint feature selection in multiple related classification or regression tasks. When doing feature selection with multiple tasks, usually one can “borrow strength” across these tasks to get a more sensitive criterion for deciding which features to select. We propose a novel method, the Multiple Inclusion Criterion (MIC), which modifies stepwise feature selection to more easily select features that are helpful across multiple tasks. Our approach allows each feature to be added to none, some, or all of the tasks. MIC is most beneficial for selecting a small set of predictive features from a large pool of potential features, as is common in genomic and biological datasets. Experimental results on such datasets show that MIC usually outperforms other competing multi-task learning methods not only in terms of accuracy but also by building simpler and more interpretable models. |
| 关 键 词: | 回归任务; 联合特征选择; 生物数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-27：lxf |
| 阅读次数: | 88 |