Description: In the distributed processing, where common labeled data may be not
available for designing classifier ensemble, however, an ensemble solution is
necessary, traditional fixed decision aggregation could not account for class prior
mismatch or classifier dependencies in electronic technology. Previous
transductive learning strategies have several drawbacks, e.g., feasibility of the
constraints was not guaranteed and heuristic learning was applied. We overcome
these problems by developing improved iterative scaling (IIS) algorithm for
optimal solution. This method is shown to achieve improved decision accuracy
over the earlier approaches in electronic technology
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