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