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
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