Rare Category Detection for Spatial Data
published: Jan. 15, 2009, recorded: November 2008, views: 945
Report a problem or upload filesIf 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.
Given an unlabeled unbalanced data set, the goal of rare category detection is to discover examples from the minority classes with a few label requests. Rare category detection is an open challenge in machine learning, and it has a lot of applications, such as financial fraud detection, network intrusion detection, astronomy, spam image detection, etc. In this talk, I will introduce two methods for rare category detection with spatial data. The first one essentially performs local density differential sampling, and it requires the prior information about the data set as input. The second one is based on specially designed exponential families, and it is prior-free. Experimental results demonstrate the effectiveness of these methods on different real data sets.
Download slides: cmulls08_he_rcd_01.pdf (664.3 KB)
Download slides: cmulls08_he_rcd_01.ppt (3.2 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !