图 书 馆
基于层次均值漂移的分类检测Category Detection Using Hierarchical Mean Shift |
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| 课程网址: | http://videolectures.net/kdd09_wong_cduhms/ |
| 主讲教师: | Weng-Keen Wong |
| 开课单位: | 俄勒冈州立大学 |
| 开课时间: | 2009-08-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning that can help address this issue using a "human-in-the-loop" approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to bring to the user's attention a representative data point from each category in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as {\it rare} category detection. We present a new approach to rare category detection based on hierarchical mean shift. In our approach, a hierarchy is created by repeatedly applying mean shift with an increasing bandwidth on the data. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the dataset properties such as the total number of categories or the prior probabilities of the categories. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques. |
| 关 键 词: | 识别异常; 统计学; 类别检测 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-10：lxf |
| 阅读次数: | 25 |