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基于相似度衡量的决策树自适应迁移

DOI: 10.3724/SP.J.1004.2013.02186, PP. 2186-2192

Keywords: 迁移学习,决策树,相似度,亲和系数

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Abstract:

?如何解决迁移学习中的负迁移问题并合理把握迁移的时机与方法,是影响迁移学习广泛应用的关键点.针对这个问题,提出一种基于相似度衡量机制的决策树自适应迁移方法(Self-adaptivetransferfordecisiontreesbasedonasimilaritymetric,STDT).首先,根据源任务数据集是否允许访问,自适应地采用成分预测概率或路径预测概率对决策树间的相似性进行判定,其亲和系数作为量化衡量关联任务相似程度的依据.然后,根据多源判定条件确定是否采用多源集成迁移,并将相似度归一化后依次分配给待迁移源决策树作为迁移权值.最后,对源决策树进行集成迁移以辅助目标任务实现决策.基于UCI机器学习库的仿真结果说明,与多源迁移加权求和算法(Weightedsumrule,WSR)和MS-TrAdaBoost相比,STDT能够在保证决策精度的前提下实现更为快速的迁移.

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