Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records

Abstract

Abstract-Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed co-evolutionary cloud model. First, the framework of N-populations distributed co-evolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the rewards of different subpopulations' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent dataset. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions are adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator's robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment cortical surfaces of the neonatal brain 3D-MRI records, and the DCCAEDR shows the superior competitive results, when compared with the representative algorithms.Index Terms-Attribute reduction accelerator, bounded rationality region, distributed co-evolutionary cloud, equilibrium dominance strategy, MapReduce framework.