An Experimental Study of Methods Combining Multiple Classifiers - Diversified both by Feature Selection and Bootstrap Sampling

Ensemble approaches are learning algorithms that construct a set of classifiers and then classify new instances by combining their predictions. These approaches can outperform single classifiers on wide range of classification problems. In this paper we proposed an extension of the bagging classifier integrating it with feature subset selection. Moreover, we examined the usage of other methods for integrating answers of these sub-classifiers, in particular a dynamic voting instead of simple voting combination rule. The extended bagging classifier (with induced decision trees as base sub-classifiers) was evaluated in an experimental comparative study with standard approaches.