Wrapper feature selection with partially labeled data

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In this paper, we propose a new feature selection approach with partially labeled training examples in the multi-class classification setting. It is based on a new modification of the genetic algorithm that creates and evaluates candidate feature subsets during an evolutionary process, taking into account feature weights and recursively eliminating irrelevant features. To increase the variety of data, unlabeled observations are employed in the feature selection process, namely by pseudo-labeling them using a self-learning algorithm with a recently proposed transductive policy. Empirical results on different data sets show the effectiveness of our method compared to several state-of-the-art semi-supervised feature selection approaches.