An ensemble of filters and classifiers for microarray data classification

In this paper a new framework for feature selection consisting of an ensemble of filters and classifiers is described. Five filters, based on different metrics, were employed. Each filter selects a different subset of features which is used to train and to test a specific classifier. The outputs of these five classifiers are combined by simple voting. In this study three well-known classifiers were employed for the classification task: C4.5, naive-Bayes and IB1. The rationale of the ensemble is to reduce the variability of the features selected by filters in different classification domains. Its adequacy was demonstrated by employing 10 microarray data sets.

► A new ensemble of filters and classifiers for feature selection is proposed. ► We combine five filters based on different metrics. ► After filtering, a classifier is trained and the result is obtained by simple voting. ► We test the ensemble over a difficult scenario: DNA microarray data. ► The ensemble obtained better results than applying the filters individually.