Title
Horizontal Case Representation
Abstract
We present a new case representation that seeks to make case-based reasoning (CBR) more suited to real world applications. We propose a horizontal representation that is composed of two features, one to represent the problem and one to represent the solution. We also present a similarity metric tailored to our representation. Rather than parametrizing the distance function with weights, it requires one parameter that recommends the cardinality of values for new problems to be solved by the system. Our representation is less restrictive during case acquisition as it does not constrain how non-experts can populate cases and it requires less knowledge engineering effort than the traditional method. We compare our representation to the traditional case representation and show that it is superior when cases are incomplete. Finally, we illustrate the effectiveness of our representation in a real world application, where the demarcation between problem and solution is blurred.
Year
DOI
Venue
2008
10.1007/978-3-540-85502-6_37
European Workshop on Case-Based Reasoning
Keywords
Field
DocType
traditional method,knowledge engineering effort,new case representation,horizontal representation,case-based reasoning,new problem,traditional case representation,case acquisition,horizontal case representation,distance function,real world application,case base reasoning,knowledge engineering
Algorithm,Cardinality,Metric (mathematics),Theoretical computer science,Case base,Knowledge engineering,Mathematics
Conference
Volume
ISSN
Citations 
5239
0302-9743
1
PageRank 
References 
Authors
0.38
13
3
Name
Order
Citations
PageRank
Rosina Weber133434.42
Sidath Gunawardena282.16
Craig Macdonald32588178.50