  In this paper, we propose a new wrapper feature selection approach with
partially labeled training examples where unlabeled observations are
pseudo-labeled using the predictions of an initial classifier trained on the
labeled training set. The wrapper is composed of a genetic algorithm for
proposing new feature subsets, and an evaluation measure for scoring the
different feature subsets. The selection of feature subsets is done by
assigning weights to characteristics and recursively eliminating those that are
irrelevant. The selection criterion is based on a new multi-class
$\mathcal{C}$-bound that explicitly takes into account the mislabeling errors
induced by the pseudo-labeling mechanism, using a probabilistic error model.
Empirical results on different data sets show the effectiveness of our
framework compared to several state-of-the-art semi-supervised feature
selection approaches.
