DocumentCode :
2845980
Title :
Boosting the Performance of CBR Applications with jCOLIBRI
Author :
Recio-Garcia, Juan A. ; Diaz-Agudo, Belen ; Gonzalez-Calero, Pedro Antonio
Author_Institution :
Dept. of Software Eng. & Artificial Intell., Univ. Complutense de Madrid, Madrid, Spain
fYear :
2009
fDate :
2-4 Nov. 2009
Firstpage :
276
Lastpage :
283
Abstract :
jCOLIBRI is currently a reference platform in the CBR community for building CBR systems that includes facilities to design different types of CBR applications. In this paper we focus in some recently included tools that allow the improvement of performance of previously designed applications. These optimization tools mainly facilitate to adjust features on large case bases like clustering and noise reduction techniques, and to adjust processes like refine similarity metrics through case base visualization, parallelization of retrieval or distribution of the case base and reasoning thought different agents. We present the tools and exemplify how to use them in a real scenario. We have developed an experiment for the automatic classification of a textual case base made of 1500 academic journals belonging to 20 different areas.
Keywords :
case-based reasoning; optimisation; CBR applications; case-based reasoning; clustering; jCOLIBRI; noise reduction; optimization; similarity metrics; textual case classification; Application software; Artificial intelligence; Boosting; Buildings; Noise reduction; Recommender systems; Software engineering; Software prototyping; Software tools; Visualization; CBR; Case-Based Reasoning; jCOLIBRI;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on
Conference_Location :
Newark, NJ
ISSN :
1082-3409
Print_ISBN :
978-1-4244-5619-2
Electronic_ISBN :
1082-3409
Type :
conf
DOI :
10.1109/ICTAI.2009.130
Filename :
5365080
Link To Document :
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