TY - GEN
T1 - Maintaining case-based reasoning systems using fuzzy decision trees1
AU - Shiu, Chi Keung Simon
AU - Sun, Cai Hung
AU - Wang, Xi Zhao
AU - Yeung, Daniel So
PY - 2000/1/1
Y1 - 2000/1/1
N2 - This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction - a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees. Firstly, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases will be carried out to identify different concepts in the case-base using the acquired feature knowledge. Thirdly, adaptation rules will be mined for each concept using fuzzy decision trees. Finally, a selection strategy based on the concepts of ε -coverage and ε -reachability is used to select representative cases. The effectiveness of the method is demonstrated experimentally using two sets of testing data.
AB - This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction - a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees. Firstly, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases will be carried out to identify different concepts in the case-base using the acquired feature knowledge. Thirdly, adaptation rules will be mined for each concept using fuzzy decision trees. Finally, a selection strategy based on the concepts of ε -coverage and ε -reachability is used to select representative cases. The effectiveness of the method is demonstrated experimentally using two sets of testing data.
UR - http://www.scopus.com/inward/record.url?scp=84948144350&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
SN - 3540679332
SN - 9783540679332
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 285
EP - 296
BT - Advances in Case-Based Reasoning - 5th European Workshop, EWCBR 2000, Proceedings
PB - Springer Verlag
T2 - 5th European Workshop on Case-Based Reasoning, EWCBR 2000
Y2 - 6 September 2000 through 9 September 2000
ER -