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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：NEURAL PROCESSING LETTERS

关键字：Dynamic ensemble selection (DES) Greedy randomized adaptive search procedure (GRASP) Path-relinking Variable neighborhood search (VNS) Greedy randomized DES combined with Path-Relinking VNS and Grasp (DyPReVNsGraspEnS)

摘要：This work proposes a novel greedy randomized dynamic ensemble selection (DES) algorithm combined with path-relinking, variable neighborhood search (VNS) and greedy randomized adaptive search procedure (GRASP) algorithms, abbreviated DyPReVNsGraspEnS. Instead of simply selecting the classifiers with the best competence under certain criterions, DyPReVNsGraspEnS makes greedy randomized selection of appropriate classifiers to form the ensemble. It realizes random multi-start search, is capable to easily escape from the local optimums, and possesses a high probability to find the optimal subensemble. It effectively strengthens the link between iterations and solves the problem of no memory by integrating the path-relinking technique. Moreover, the algorithm properly extends the neighborhood with the help of VNS, having a higher competence of global optimization. Most important of all, DyPReVNsGraspEnS is developed based upon the framework of DES paradigm, therefore, it inherits numerous advantages of DES. Empirical investigations are conducted based on twelve benchmark datasets, including six real-world credit/finance datasets and six datasets drawn from other different fields with more than two classes. To clearly articulate the performance of each strategy in the proposed algorithm, experiments for the incorporation of different strategies are implemented separately on all the datasets. The experimental results demonstrate that, in comparison with other classical ensemble learning techniques, DyPReVNsGraspEnS achieves significantly superior generalization performance. And it can not only be used for regions like credit risk assessment, but is applicable to much broader application fields as well.

ISSN号：1370-4621

是否译文：否

发表时间：2018-04-01

合写作者：叶锐

通讯作者：戴群