Combined feature selection and similarity modelling in case-based reasoning using hierarchical memetic algorithm

This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. T...

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Bibliographic Details
Published inIEEE Congress on Evolutionary Computation pp. 1 - 6
Main Authors Ning Xiong, Funk, Peter
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
Accuracy
Biological cells
Cognition
Computational modeling
Libraries
Memetics
Sonar
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Summary:This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. The objective of the memetic search is to optimize the possibility distributions derived for individual cases in the case library under a leave-one-out procedure. The information about the importance of selected features is revealed from the magnitudes of parameters of the learned similarity model. The effectiveness of the proposed approach has been shown by evaluation results on the benchmark data sets from the UCI repository and in comparisons with other machine learning techniques.
ISBN:1424469090
9781424469093
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2010.5586421