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Bootstrap feature selection for ensemble classifiers
rakkrit Duangsoithong and Terry Windeatt
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6171 LNAI:28-41 2010
Volume 6171,
,
2010.
AbstractAbstract. Small number of samples with high dimensional feature space
leads to degradation of classifier performance for machine learning, statistics
and data mining systems. This paper presents a bootstrap feature
selection for ensemble classifiers to deal with this problem and compares
with traditional feature selection for ensemble (select optimal features
from whole dataset before bootstrap selected data). Four base classifiers:
Multilayer Perceptron, Support Vector Machines, Naive Bayes and Decision
Tree are used to evaluate the performance of UCI machine learning
repository and causal discovery datasets. Bootstrap feature selection
algorithm provides slightly better accuracy than traditional feature selection
for ensemble classifiers.
Keywords: Bootstrap, feature selection, ensemble classifiers. [Edit]
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