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多标签集成学习Multi-Label Ensemble Learning |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_tang_multilabel/ |
| 主讲教师: | Wenbin Tang |
| 开课单位: | 清华大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners. However, the generalization ability of such individual learner can be weak. It is well known that ensemble learning can effectively improve the generalization ability of learning systems by constructing multiple base learners and the performance of an ensemble is related to the both accuracy and diversity of base learners. In this paper, we study the problem of multilabel ensemble learning. Specifically, we aim at improving the generalization ability of multi-label learning systems by constructing a group of multilabel base learners which are both accurate and diverse. We propose a novel solution, called EnML, to effectively augment the accuracy as well as the diversity of multi-label base learners. In detail, we design two objective functions to evaluate the accuracy and diversity of multilabel base learners, respectively, and EnML simultaneously optimizes these two objectives with an evolutionary multi-objective optimization method. Experiments on real-world multi-label learning tasks validate the effectiveness of our approach against other well-established methods. |
| 关 键 词: | 多标签学习; 泛化能力; 集成学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-29：nkq |
| 阅读次数: | 172 |