【摘要】 针对常见分类算法在全局和局部区域性能不一致的问题,提出了双层分类策略及其实现算法。双层分类策略的思想是离线地建立全局分类器,当全局分类器决策信用度低于指定阈值时,在线生成局部分类器进行决策修正。实现算法以支持向量机(support vectormachine,SVM)和模糊分类器(fuzzy classifier)作为全局与局部分类器,命名为SFC。为全局分类器定义了SVM决策信用度的评估机制,并以此给出局部分类器的启动条件。为局部分类器设计了基于新测度的模糊隶属度函数完成决策修正。实验结果表明,SFC显著提高了单一分类器的性能,可达到较好的分类效果。由此说明双层分类思想是正确且有效的,可作为一种通用思想对应多种具体实现算法。更多还原

【Abstract】 To address the unsteady performance of common classifier in global environment and local region,this paper proposed a double-classification strategy and its implementation algorithm.The idea of the strategy was to create global classifier offline and local classifier online.When the confidence of global classifier’s decision was below some threshold,constructed local classifier to refine the global decision.The implementation algorithm employed support vector machine(SVM) and fuzzy classifier as global and local classifier respectively,named as SFC.Defined the confidence scoring method of SVM decision.Based on that,gave motivation condition of local classifier.For local classifier,defined a new metric to facilitate the work of fuzzy membership function.Empirical evidence shows SFC improves classification performance of the single classifier,and exhibites fine classification ability in applications.SFC’s good behaviors lead to the conclusion that double-classification idea is correct and valid,and it can be realized into multi implementations.更多还原