Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
The Journal of Machine Learning Research
Visualizing and Evaluating Complexity of Textual Case Bases
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Evaluation Measures for TCBR Systems
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
Complexity profiling for informed case-base editing
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Measuring the complexity of a collection of documents
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Introspective knowledge revision in textual case-based reasoning
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Reexamination of CBR hypothesis
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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In TCBR, complexity refers to the extent to which similar problems have similar solutions. Casebase complexity measures proposed are based on the premise that a casebase is simple if similar problems have similar solutions. We observe, however, that such measures are vulnerable to choice of solution side representations, and hence may not be meaningful unless similarities between solution components of cases are shown to corroborate with human judgements. In this paper, we redefine the goal of complexity measurements and explore issues in estimating solution side similarities. A second limitation of earlier approaches is that they critically rely on the choice of one or more parameters. We present two parameter-free complexity measures, and propose a visualization scheme for casebase maintenance. Evaluation over diverse textual casebases show their superiority over earlier measures.