Avoiding bias when aggregating relational data with degree disparity (2003)
Cached
Download Links
- [www.hpl.hp.com]
- [kdl.cs.umass.edu]
- [kdl.cs.umass.edu]
- [kdl.cs.umass.edu]
- [www.cs.purdue.edu]
- [www.cs.umass.edu]
- [www.cs.cornell.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | In Proceedings of the 20th International Conference on Machine Learning |
| Citations: | 19 - 11 self |
BibTeX
@INPROCEEDINGS{Jensen03avoidingbias,
author = {David Jensen and Jennifer Neville and Michael Hay},
title = {Avoiding bias when aggregating relational data with degree disparity},
booktitle = {In Proceedings of the 20th International Conference on Machine Learning},
year = {2003},
pages = {274--281}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
A common characteristic of relational data sets —degree disparity—can lead relational learning algorithms to discover misleading correlations. Degree disparity occurs when the frequency of a relation is correlated with the values of the target variable. In such cases, aggregation functions used by many relational learning algorithms will result in misleading correlations and added complexity in models. We examine this problem through a combination of simulations and experiments. We show how two novel hypothesis testing procedures can adjust for the effects of using aggregation functions in the presence of degree disparity. 1.
