Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Unsupervised case memory organization: analysing computational time and soft computing capabilities
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
When Similar Problems Don't Have Similar Solutions
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Methodology for Analyzing Case Retrieval from a Clustered Case Memory
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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Case-Based Reasoning (CBR) systems solve new problems using others which have been previously resolved. The knowledge is composed of a set of cases stored in a case memory, where each one describes a situation in terms of a set of features. Therefore, the size and organization of the case memory influences in the computational time needed to solve new situations. We organize the memory using Self-Organization Maps, which group cases with similar properties into patterns. Thus, CBR is able to do a selective retrieval using only the cases from the most suitable pattern. However, the data complexity may hinder the identification of patterns and it may degrade the accuracy rate. This work analyses the successful application of this approach by doing a previous data complexity characterization. Relationships between the performance and some measures of class separability and the discriminative power of attributes are also found.