Paper Info
Title
Two stages of case-based reasoning - Integrating genetic algorithm with data mining mechanism
Abstract
Case-based reasoning (CBR) is a paradigm, concept and instinctive mechanism for problem solving. Recently, CBR has been widely integrated with some AI algorithms and applied to various kinds of problems. The ill-defined and unstructured problems are suitably solved by CBR. This research proposes a hybrid CBR mechanism including two stages. In stage I, the genetic algorithm is adopted to improve efficiency of case retrieving process. Compared to traditional CBR, the proposed mechanism could reduce about 14% case evaluations, but still achieved 90% satisfactory results. In stage II, the knowledge discovering and data mining (KDD) processes are implemented to produce the refined information from the retrieved cases. Because these retrieved cases and target problem satisfy similar or even same conditions, the outcome of KDD would be more valuable for reference. In addition to retrieved cases of stage I, the proposed mechanism provides direction and relevant knowledge for decision makers in their decision supporting and revising processes in stage II. The proposed CBR mechanism also deals with efficiency and outcome quality issues of traditional CBR.
Year
DOI
Venue
2008
10.1016/j.eswa.2007.06.027
Expert Syst. Appl.
Keywords
Field
DocType
proposed cbr mechanism,genetic algorithm,relevant knowledge,case retrieve,hybrid cbr mechanism,decision maker,outcome quality issue,case-base reasoning,traditional cbr,stage ii,data mining mechanism,case evaluation,case-based reasoning,instinctive mechanism,kdd,proposed mechanism,data mining,case base reasoning,decision support,satisfiability
Data mining,Computer science,Artificial intelligence,Case-based reasoning,Machine learning,Genetic algorithm
Journal
Volume
Issue
ISSN
35
1-2
Expert Systems With Applications
Citations 
PageRank 
References 
15
0.83
13
Authors
2
Name
Order
Citations
PageRank
hengli yang134427.53
Cheng-Shu Wang2181.25