In order to further enhance the global performance of evolutionary algorithms for solving minimum attribute reduction, a quantum-cloud-feedback-based attribute equilibrium dominance ensemble reduction algorithm (QCAEDER) with co-evolutionary elitists is proposed. First, an adaptive strategy of quantum revolving angle update operation based on cloud mode feedback is designed, so that the search space scope of quantum frog elitists can be adaptively controlled under the guidance of qualitative knowledge of cloud model and penalty factor feedback. Second, the attribute decomposition framework of co-evolutionary elitists with equilibrium dominance under the bounded rationality regions is constructed, in order to assist the quantum frog elitists necessary to attain the stable status of Nash equilibrium dominance by the average weighted credits. Third, the quantum frog elitists can extract attribute reduction subsets in the respective regions of equilibrium dominance by using the ensemble operation mechanism. Consequently, the global optimal solution of ensemble feature set can be achieved stably. The experimental results show that the proposed algorithm has achieved a higher performance on the efficiency, precision and stability of global optimal attribute reduction. Furthermore, the validation performed on brain MRIs electronic medical records of gestational age neonate demonstrates its strong advantage for real-world applications.