Abstract
Abstract This study looks at the relationships between different methods of classifier combination and different measures of diversity. We considered 10 combination methods and 10 measures of diversity on two benchmark data sets. The relationship was sought on ensembles of three classifiers built on all possible partitions of the respective feature sets into subsets of pre-specified sizes. The only positive finding was that the Double-Fault measure of diversity and the measure of difficulty both showed reasonable correlation with Majority Vote and Naive Bayes combinations. Since both these measures have an indirect connection to the ensemble accuracy, this result was not unexpected. However, our experiments did not detect a consistent relationship between the other measures of diversity and the 10 combination methods.
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