Exploration of classification confidence in ensemble learning
We give a new definition of margin based on classification confidence of base classifiers in ensemble learning. We construct optimal objective functions based on margin distribution for obtaining weights of base classifiers. Difference strategies to utilize the weights and classification confidence in the final decision are tried. Extensive experiments are conducted to compare different solutions and an optimal solution is derived.
classification confidence ensemble learning ordered aggregation ensemble margin