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Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most...
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...
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