Linked Life Data

16865914

Source:http://linkedlifedata.com/resource/pubmed/id/16865914

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-7-26
pubmed:abstractText
The potential of Case-Based Reasoning to use the knowledge gained from past experiences to solve problematic situations has made this Artificial Intelligence technique a useful decision support tool in different environmental domains such as wastewater treatment. Case-Based Reasoning tools automatically identify similarities between present and previous situations (cases) and reuse the experiences gained from the previous situations to solve current problems. Case retrieval can be considered to be the most important step in the process of Case-Based Reasoning. In the present study we propose incorporating a relevance network in order to increase the accuracy and the efficiency of case retrieval. The result is a context-sensitive feature-weighting methodology capable of defining the model of relationships between the different attributes or features that define the context in which Case-Based Reasoning is applied. These features affect the retrieval procedure directly. The feature's degree of relevance in the network is easily translated into a set of simple rules and applied during case retrieval, specifically during the similarity calculation. The results obtained in the present study show significant improvements in the accuracy of case retrieval. With the approach presented here experts considered more than 90% of the retrieved cases to be completely relevant according to the knowledge these cases provided for dealing with solids separation problems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0959-3330
pubmed:author
pubmed:issnType
Print
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
585-96
pubmed:dateRevised
2007-3-21
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Improving the efficiency of case-based reasoning to deal with activated sludge solids separation problems.
pubmed:affiliation
Laboratory of Chemical and Environmental Engineering (LEQUIA), University of Girona, Campus Montilivi s/n, E-17071 Girona, Spain.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't