# Defining Diversity in Ensemble Learning

I have a few questions regarding on how diversity is defined since I've seen differing definitions in different papers.

In the paper "Measures of Diversity in Classifier Ensembles and their Relationship with Ensemble Accuracy" by Ludmila I. Kuncheva (2003) I saw this definition:

When classifiers output class labels, the classification error can be
decomposed into bias and variance terms (also called ‘spread’) (Bauer & Kohavi, 1999; Breiman, 1999; Kohavi & Wolpert, 1996) or into bias and spread > terms. In both cases the second term can be taken as the diversity of the ensemble.

However in other places I've seen that the diversity is based on how strongly correlated are the base learners. The less correlated they are the more diverse they are.

Or is there no difference since $Corr(X,Y) = \frac{Cov(X,Y)}{\sqrt{Var(X)Var(Y)}}$. The higher the variance the smaller the correlation and the larger the diversity will be(?)

As well, a high level definition I've heard too is that diversity measures the number of coincident errors committed by classifiers in an ensemble. Would this be a true statement still if the diversity was really the variance or correlation among classifiers?