图 书 馆
密度估计与类概率相结合的一类分类One-class Classification by Combining Density and Class Probability Estimation |
|
| 课程网址: | http://videolectures.net/ecmlpkdd08_hempstalk_ocbc/ |
| 主讲教师: | Kathryn Hempstalk, Eibe Frank, Ian H. Witten |
| 开课单位: | 怀卡托大学 |
| 开课时间: | 2008-10-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines. |
| 关 键 词: | 异常值; 密度估计技术; 标准分类算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-23：lxf |
| 阅读次数: | 82 |