Data classification using an ensemble of filters

Ensemble learning has been the focus of much attention, based on the assumption that combining the output of multiple experts is better than the output of any single expert. Many methods have been proposed of which bagging and boosting were the most popular. In this research, the idea of ensembling is adapted for feature selection. We propose an ensemble of filters for classification, aimed at achieving a good classification performance together with a reduction in the input dimensionality. With this approach, we try to overcome the problem of selecting an appropriate method for each problem at hand, as it is overly dependent on the characteristics of the datasets. The adequacy of using an ensemble of filters rather than a single filter was demonstrated on synthetic and real data, paving the way for its final application over a challenging scenario such as DNA microarray classification.