Sciweavers

Share
MCS
2010
Springer

"Good" and "Bad" Diversity in Majority Vote Ensembles

10 years 10 months ago
"Good" and "Bad" Diversity in Majority Vote Ensembles
Although diversity in classifier ensembles is desirable, its relationship with the ensemble accuracy is not straightforward. Here we derive a decomposition of the majority vote error into three terms: average individual accuracy, “good” diversity and “bad diversity”. The good diversity term is taken out of the individual error whereas the bad diversity term is added to it. We relate the two diversity terms to the majority vote limits defined previously (the patterns of success and failure). A simulation study demonstrates how the proposed decomposition can be used to gain insights about majority vote classifier ensembles.
Gavin Brown, Ludmila I. Kuncheva
Added 18 May 2010
Updated 18 May 2010
Type Conference
Year 2010
Where MCS
Authors Gavin Brown, Ludmila I. Kuncheva
Comments (0)
books