Bootstrap feature selection for ensemble classifiers

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. © 2010 Springer-Verlag Berlin Heidelberg.