计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 6-10.doi: 10.11896/j.issn.1002-137X.2018.10.002
• 2018 年中国粒计算与知识发现学术会议 • 上一篇 下一篇
邢颖1, 李德玉1,2, 王素格1,2
XING Ying1, LI De-yu1,2, WANG Su-ge1,2
摘要: 在现实决策中,代价敏感问题是影响人类决策的重要因素之一,许多研究者致力于降低决策的代价。现阶段,在粗糙集领域中,研究者多基于DTRS模型且仅考虑某一种代价,不够全面。针对以上问题,利用序贯三支决策模型对两种代价的敏感性,通过多层次粒结构可以有效降低决策总代价,且能够更好地模拟人类动态渐进的决策过程。在序贯三支决策模型的基础上,构造了多层次粒结构;将各个属性的测试代价与其分类能力相关联,从信息熵的角度为其设置测试代价;与此同时,将属性约简与序贯三支决策相结合,利用基于代价最小准则的属性约简去除冗余属性及不相关属性对代价的影响。在7个UCI数据集上的实验结果显示,在保证较高准确度的同时,决策的总代价平均下降了26%左右,充分验证了该方法的有效性。
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