图 书 馆
将新颖的类检测与概念漂移数据流的分类相结合Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_khan_incdccdds/ |
| 主讲教师: | Latifur Khan |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In a typical data stream classification task, it is assumed that the total number of classes are fixed. This assumption may not be valid in a real streaming environment, where new classes may evolve. Traditional data stream classification techniques are not capable of recognizing novel class instances until the appearance of the novel class is manually identified, and labeled instances of that class are presented to the learning algorithm for training. The problem becomes more challenging in the presence of concept-drift, when the underlying data distribution changes over time. We propose a novel and efficient technique that can automatically detect the emergence of a novel class in the presence of concept-drift by quantifying cohesion among unlabeled test instances, and separation of the test instances from training instances. Our approach is non-parametric, meaning, it does not assume any underlying distributions of data. Comparison with the state-of-the-art stream classification techniques prove the superiority of our approach. |
| 关 键 词: | 数据流; 学习算法; 概念漂移 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-31：yumf |
| 阅读次数: | 66 |