摘要
讨论了经典粗糙集理论在特征项约简过程中,造成的范例相似度测量误差问题,并提出用相似粗糙集约简特征项和获取特征项权重的过程与算法,力求提高约简的精确性和保持约简后范例库的良好性能。
This paper discusses a deviation problem of measuring case similarity, which happens in the process of features reduction with classical rough set theory. In order to improve reduction accuracy and keep good quality of reduced case base, this paper proposes an algorithm based on similarity rough set theory to reduce features and compute features weight.
出处
《计算机工程》
CAS
CSCD
北大核心
2003年第19期50-51,116,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60273043)
安徽省教育厅自然科学基金资助项目(2002kj004)
关键词
相似粗糙集
范例推理
特征项约简
差别矩阵
特征项权值
Similarity rough set
Case-based reasoning(CBR)
Features reduction
indiscernibility matrix
Features weight
