Relevance and Redundancy Analysis for Ensemble Classifiers
Identifieur interne : 000849 ( Main/Exploration ); précédent : 000848; suivant : 000850Relevance and Redundancy Analysis for Ensemble Classifiers
Auteurs : Rakkrit Duangsoithong [Royaume-Uni] ; Terry Windeatt [Royaume-Uni]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: In machine learning systems, especially in medical applications, clinical datasets usually contain high dimensional feature spaces with relatively few samples that lead to poor classifier performance. To overcome this problem, feature selection and ensemble classification are applied in order to improve accuracy and stability. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using five datasets and compared with floating search method. Eliminating redundant features provides better accuracy and computational time than removing irrelevant features of the ensemble.
Url:
DOI: 10.1007/978-3-642-03070-3_16
Affiliations:
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