  In this paper entropy based methods are compared and used to measure
structural diversity of an ensemble of 21 classifiers. This measure is mostly
applied in ecology, whereby species counts are used as a measure of diversity.
The measures used were Shannon entropy, Simpsons and the Berger Parker
diversity indexes. As the diversity indexes increased so did the accuracy of
the ensemble. An ensemble dominated by classifiers with the same structure
produced poor accuracy. Uncertainty rule from information theory was also used
to further define diversity. Genetic algorithms were used to find the optimal
ensemble by using the diversity indices as the cost function. The method of
voting was used to aggregate the decisions.
