图 书 馆
基于实例学习与逻辑回归相结合的多标签分类方法Combining Instance-Based Learning and Logistic Regression for Multi-Label Classification |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_cheng_cibl/ |
| 主讲教师: | Weiwei Cheng |
| 开课单位: | 菲利普森马尔堡大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture interdependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction. |
| 关 键 词: | 多标签分类; 最近邻估计原理; 逻辑回归 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-24：cwx |
| 阅读次数: | 81 |