Using Feature Selection Approaches to Find the Dependent Features View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Qin Yang , Elham Salehi , Robin Gras

ABSTRACT

Dependencies among the features can decrease the performance and efficiency in many algorithms. Traditional methods can only find the linear dependencies or the dependencies among few features. In our research, we try to use feature selection approaches for finding dependencies. We use and compare Relief, CFS, NB-GA and NB-BOA as feature selection approaches to find the dependent features among our artificial data. Unexpectedly, Relief has the best performance in our experiments, even better than NB-BOA, which is a population-based evolutionary algorithm that used the population distribution information to find the dependent features. It may be because some weak ”link strengths” between features or due to the fact that Naïve Bayes classifier which is used in these wrapper approaches cannot represent the dependencies between features. However, the exact reason for these results still is an open problem for our future work. More... »

PAGES

487-494

Book

TITLE

Artificial Intelligence and Soft Computing

ISBN

978-3-642-13207-0
978-3-642-13208-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-13208-7_61

DOI

http://dx.doi.org/10.1007/978-3-642-13208-7_61

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015184665


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