There exists a model of using several ‘experts’ or classifiers in order to increase the relevancy of a result. In the fish example from Duda, Hart, Stork, a group of experts are asked to determine if a fish is diseased. 9 of them say it’s not, but one disagrees. How do you ask a computer system to choose between the majority or minority opinion. That one dissenter may have specialized knowledge that gives him an advantage and thus has the correct answer. In a human situation, those 10 experts would argue with each other until perhaps others are convinced.
Can one classifier learn from another? That may be difficult in practice. If the majority classifiers could generate fantasy problems and ask the minority dissenter to solve these problems we can determine if the minority opinion should be given greater weight. If the minority expert does better with this type of problem, his ‘opinion’ should be given greater weight; perhaps enough weight to offset the majority. The majority group could then learn from this new data and adjust their weights accordingly.