One-class Classification by Combining Density and Class Probability Estimation

author: Kathryn Hempstalk, Computer Science Department, University of Waikato
author: Eibe Frank, Computer Science Department, University of Waikato
author: Ian H. Witten, Computer Science Department, University of Waikato
published: Oct. 10, 2008,   recorded: September 2008,   views: 787
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Sung Hee , October 13, 2009 at 8:14 a.m.:

Hello !
Content of this is really usefull to me. but It is very hard to watch it becase often buffering is borthering to focus on lecture and suddenly voice is mutted.
how can we solve this problem and watch it without annoying ?

thanks in advance

Write your own review or comment:

make sure you have javascript enabled or clear this field: