说明：  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