摘要
基于范例推理的气象灾害预测领域,从气象数据库中提取出典型的源范例是首要的步骤,而获取高质量且无噪声干扰的气象范例,减少范例存储的时间和空间复杂度是其主要目标。提出了利用相似粗糙集进行气象范例提取的算法,通过简化相似天气的无向图,自动从原始天气数据中提取典型范例。该算法能较好的处理噪声的干扰,并能直接处理连续数值型属性,避免了复杂的属性离散化的计算。实验结果验证了算法的可行性和有效性。
Case selection from weather database is a key step in disaster weather forecasts based on CBR. The selection of representative weather cases without noise and reduces time and space complexity are its essential target. The SRS algorithm based on similarity-based rough set theory is proposed. By reducing undirected graph, it can selects a reasonable number of the typical cases from a large data set for future case-based reasoning tasks. It also can handle noise and inconsistent data. Experimental result has confirmed the algorithm feasibility and the validity.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第15期3749-3751,共3页
Computer Engineering and Design
基金
教育部优秀人才支持计划基金项目(NCET-04-0513)
南京信息工程大学科研基金项目(Y618)
关键词
相似粗糙集
相似关系
范例推理
知识表达系统
无向图
similarity-rough set
similarity relation
case-based reasoning
information systems
undirected graph
