Semisupervised Feature Selection with Universum

The Universum data, defined as a set of unlabeled examples that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful information in the same domain as the problem at hand. Universum data have been proved effective in improving learning performance in many tasks, such as classification and clustering. Inspired by its favorable performance, we address a novel semisupervised feature selection problem in this paper, called semisupervised feature selection with Universum, that can simultaneously exploit the unlabeled data and the Universum data. The experiments on several UCI data sets are presented to show that the proposed algorithms can achieve superior performances over conventional unsupervised and supervised methods.