Sciweavers

Share
DMIN
2006

Ensemble Selection Using Diversity Networks

9 years 1 months ago
Ensemble Selection Using Diversity Networks
- An ideal ensemble is composed of base classifiers that perform well and that have minimal overlap in their errors. Eliminating classifiers from an ensemble based on a criterion that reflects poor classification performance and error redundancy with peer classifiers can improve ensemble performance. The Diversity Networks method asymmetrically evaluates each pair of classifiers as a linear combination of individual performance and diversity. This measure is used to prune the ensemble gradually to find a nearly optimal ensemble.
Qiang Ye, Paul W. Munro
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where DMIN
Authors Qiang Ye, Paul W. Munro
Comments (0)
books