Semi-supervised feature selection via multiobjective optimization

In previous work, we have shown that both unsupervised feature selection and the semi-supervised clustering problem can be usefully formulated as multiobjective optimization problems. In this paper, we discuss the logical extension of this prior work to cover the problem of semi-supervised feature selection. Our extensive experimental results provide evidence for the advantages of semi-supervised feature selection when both labelled and unlabelled data are available. Moreover, the particular effectiveness of a Pareto-based optimization approach can also be seen.