Application of hybrid symbolic ensembles to gene expression analyses

This paper considers a class of hybrid (heterogeneous) ensembles purely composed of symbolic elements. In learning diagnostic rules from gene expressions they demonstrate a significant improvement of accuracy with a small number of ensemble elements. This makes them suitable for learning of understa...

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Bibliographic Details
Published in2008 9th Symposium on Neural Network Applications in Electrical Engineering pp. 95 - 98
Main Authors Miskovic, V., Milosavljevic, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2008
Subjects
Accuracy
application
combined classifiers
diagnostics
Diversity methods
Diversity reception
feature selection
Gene expression
gene expressions
hybrid ensembles
Hybrid power systems
inductive learning
Learning systems
Neural networks
Radio frequency
Stacking
symbolic methods
Voting
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Summary:This paper considers a class of hybrid (heterogeneous) ensembles purely composed of symbolic elements. In learning diagnostic rules from gene expressions they demonstrate a significant improvement of accuracy with a small number of ensemble elements. This makes them suitable for learning of understandable knowledge, leading to diagnosis and its explanation in original terms (attributes).
ISBN:142442903X
9781424429035
DOI:10.1109/NEUREL.2008.4685577