PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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, pp. 28-41, 2010.

Abstract

Abstract. 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8109
Deposited By:Terry Windeatt
Deposited On:19 April 2011