SemiFREE: Semisupervised Feature Selection With Fuzzy Relevance and Redundancy

Feature selection, as an effective dimensionality reduction technique, is favored in preprocessing data. However, most existing algorithms are solely liable for labeled or unlabeled data, whereas a limited portion of real-world data is annotated with labels. In this article, we therefore propose a novel scheme named SemiFREE, i.e., semisupervised feature selection with fuzzy relevance and redundancy. First, both labeled and unlabeled samples are assigned with fuzzy decisions that allow class membership to naturally express the fuzziness or uncertainty in data labeling. Second, sample similarities in feature space and fuzzy decision are captured to induce fuzzy information measures for redefining the feature relevance and redundancy. Finally, adhering to the principle of relevance-maximization and redundancy-minimization, SemiFREE leverages the forward sequential searching strategy to identify qualified features progressively. Extensive experiments demonstrate the superiority of SemiFREE in the presence of partially labeled data against some other well-established feature selection algorithms.