Title :
Fuzzy relational learning: A new approach to case-based reasoning
Author :
Ning Xiong ; Liangjun Ma ; Shouchuan Zhang
Author_Institution :
Sch. of Innovation, Malardalen Univ., Vasteras, Sweden
Abstract :
This paper aims to develop a new approach to case-based reasoning without similarity constraint. The key to this is the case relation model which enables identification of relevant cases from a global perspective. Fuzzy linguistic rules are adopted as powerful means to represent knowledge about relevance between cases in the case relation model. The construction of fuzzy relevance rules can be realized by learning from pairs of cases in the case library. The empirical studies have demonstrated that our CBR system using fuzzy relation model can work with an extremely small number of cases while still yielding competent performance.
Keywords :
case-based reasoning; fuzzy set theory; knowledge representation; learning (artificial intelligence); CBR system; case library; case relation model; case-based reasoning; fuzzy linguistic rules; fuzzy relation model; fuzzy relational learning; fuzzy relevance rules; knowledge representation; relevant case identification; Accuracy; Cognition; Computer aided software engineering; Iris; Libraries; Silicon; Training; case-based reasoning; fuzzy rules; relational learning; relational model;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/FSKD.2013.6816266