Combining feature ranking algorithms through rank aggregation

The problem of combining multiple feature rankings into a more robust ranking is investigated. A general framework for ensemble feature ranking is proposed, alongside four instantiations of this framework using different ranking aggregation methods. An empirical evaluation using 39 UCI datasets, thr...

Full description

Saved in:
Bibliographic Details
Main Author: Prati, R. C.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Aggregates
Algorithm design and analysis
Decision trees
Prediction algorithms
Predictive models
Vectors
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The problem of combining multiple feature rankings into a more robust ranking is investigated. A general framework for ensemble feature ranking is proposed, alongside four instantiations of this framework using different ranking aggregation methods. An empirical evaluation using 39 UCI datasets, three different learning algorithms and three different performance measures enable us to reach a compelling conclusion: ensemble feature ranking do improve the quality of feature rankings. Furthermore, one of the proposed methods was able to achieve results statistically significantly better than the others.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2012.6252467