Ensemble feature selection and multiple classifier systems have recently
gained importance in machine learning. Ensemble learning improves
learning ability by combining several models, an improvement which leads
to better predictive performance than a single model. In recent years,
ensemble based feature selection approaches have been proposed in which,
multiple diverse feature selection methods are combined. These
approaches are superior to traditional feature selection techniques in
various aspects. In this paper, we propose a novel ensemble based
feature selection algorithm using Convex Concave Programming, which is
based on ensemble pruning. The optimization model in the pruning step
selects the best subset of the ensemble, simultaneously considering the
models' accuracy and diversity. The proposed algorithm was tested on
multiple data sets and learning performances are compared with various
feature selection algorithms. The empirical results shows that the
proposed algorithm performs at higher classification accuracy.