Comparison of distances for multi-label classification with PCTs

author: Valentin Gjorgjioski, Department of Knowledge Technologies, Jožef Stefan Institute
published: Nov. 4, 2011,   recorded: October 2011,   views: 3105
Categories

Slides

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

Multi-label classification has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label classification methods. These methods either transform the multi-label dataset to several simpler datasets or adapt the learning algorithm so it can handle the multiple labels. In this paper, we consider the latter approach. Namely, we use predictive clustering trees to perform multi-label classification. Furthermore, we perform an experimental comparison of four distance measures used to select the splits in the nodes of the trees. The experimental evaluation was conducted on 6 benchmark datasets using 6 different evaluation measures. The results show that, averaged overall, the Euclidean distance and the Hamming loss yield the best predictive performance.

See Also:

Download slides icon Download slides: sikdd2011_gjorgjioski_classification_01.pdf (527.6 KB)


Help icon Streaming Video Help

Link this page

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

Write your own review or comment:

make sure you have javascript enabled or clear this field: