Paper Info
Title
Combined feature selection and similarity modelling in case-based reasoning using hierarchical memetic algorithm
Abstract
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.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586421
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
uci repository,evolutionary computation,case-based reasoning,learning (artificial intelligence),similarity modelling,leave-one-out procedure,combined feature selection,case based reasoning,hierarchical memetic algorithm,machine learning,case base reasoning,feature selection,sonar,memetics,memetic algorithm,computational modeling,learning artificial intelligence,accuracy,cognition
Memetic algorithm,Data set,Feature selection,Computer science,Evolutionary computation,Sonar,Artificial intelligence,Case-based reasoning,Memetics,Cognition,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
6
0.52
References 
Authors
22
2
Name
Order
Citations
PageRank
Ning Xiong1585.90
P. Funk229122.99