Is independence good for combining classifiers?
Ludmila Ilieva Kuncheva, Christopher J. Whitaker, Catherine A. Shipp, Robert P. W. Duin · 2002
Independence between individual classifiers is typically viewed as an asset in classifier fusion. We study the limits on the majority vote accuracy when combining dependent classifiers. Q statistics are used to measure the dependence between classifiers. We show that dependent classifiers could offer a dramatic improvement over the individual accuracy. However, the relationship between dependency and accuracy of the pool is ambivalent. A synthetic experiment demonstrates the intuitive result that, in general, negative dependence is preferable.