PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Relevance and Redundancy Analysis for Ensemble Classifiers
R Duangsoithong and Terry Windeatt
In: MLDM 2009, Leipzig, Germany(2009).

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6071
Deposited By:Terry Windeatt
Deposited On:08 March 2010