南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (2): 447452.
张燕平1,2, 邹慧锦1,2,赵姝1,2
Zhang Yanping1,2, Zou Huijin1,2*,Zhao Shu1,2
摘要: 随着数据挖掘和机器学习技术在实际问题中的广泛应用, 人们越来越多的发现实际分类问题通常具有代价敏感特性 . 代价敏感的分类是指在同一分类任务中错误分类的代价是不同的 . 介绍了一种基于构造性覆盖算法
的代价敏感三支决策模型, 即将代价敏感引入到基于构造性覆盖算法的三支决策模型 . 该模型根据误分类之间的大小关系来减少正、 负覆盖的个数, 从而调整三个域, 即正域、 负域和边界域的大小 . 引入代价敏感的目的是尽可能
的减少划分损失 . 实验对比了本文的模型分类结果和基于决策粗糙集的三支决策模型, 结果表明, 本文的模型分类结果稳定, 并且能够通过改变三个域的大小, 把分类损失最小化 .
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