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.

• Type: Conference
• Year: 2006
• Where: DMIN
• Authors: Qiang Ye, Paul W. Munro
• Keywords: Base Classifiers, DMIN 2006, DMIN 2007, Ideal Ensemble, Peer Classifiers