Relevance Matrix Generation Using Sensitivity Analysis in a Case-Based Reasoning Environment

Rotem Stram; Pascal Reuss; Klaus-Dieter Althoff; Wolfram Henkel; Daniel Fischer
In: Ashok Goel; M. Belén Díaz-Agudo; Thomas Roth-Berghofer (Hrsg.). Case-Based Reasoning Research and Development. International Conference on Case-Based Reasoning (ICCBR-16), October 31 - November 2, Atlanta, GA, USA, Pages 402-412, LNAI, Vol. 9969, ISBN 978-3-319-47095-5, Springer, 2016.


Relevance matrices are a way to formalize the contribution of each attribute in a classification task. Within the CBR paradigm these matrices can be used to improve the global similarity function that outputs the similarity degree of two cases, which helps facilitate retrieval. In this work a sensitivity analysis method was developed to optimize the relevance values of each attribute of a case in a CBR environment, thus allowing an improved comparison of cases. The process begins with a statistical analysis of the values in a given dataset, and continues with an incremental update of the relevance of each attribute. The method was tested on two datasets and it was shown that the statistical analysis performs better than evenly distributed relevance values, making it a suitable initial setting for the incremental update, and that updating the values over time gives better results than the statistical analysis.



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