Maintaining case knowledge vocabulary using a new evidential attribute clustering method

被引：0

作者：

Ben Ayed, S. ^{[1
]}

Elouedi, Z. ^{[1
]}

Lefevre, E. ^{[2
]}

机构：

[1] Univ Tunis, Inst Super Gest Tunis, LARODEC, 41 Ave Liberte, Le Bardo 2000, Tunisia

[2] Univ Artois, EA 3926, LGI2A, F-62400 Bethune, France

来源：

DATA SCIENCE AND KNOWLEDGE ENGINEERING FOR SENSING DECISION SUPPORT | 2018年 / 11卷

关键词：

Case based reasoning; maintenance; case vocabulary; attribute clustering; belief function theory; feature selection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Maintaining the vocabulary of case knowledge within Case Based Reasoning (CBR) presents a crucial task to ensure a high-quality problem-solving and to improve retrieval performance for large-scale CBR systems. To do, we propose, in this paper, a method that manages uncertainty while selecting the best attributes characterizing case knowledge by using belief function theory. Actually, this method is based on a new evidential attribute clustering technique to eliminate redundant and noisy attributes describing cases.

引用

页码：347 / 354

页数：8

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