Associated multi-label fuzzy-rough feature selection

Ahead of the process of selecting a subset of relevant features, the labels commonly need to be combined into a single one for multi-label feature selection. However the existing label combination methods assume that all labels are independent of each other and consequently suffer from high computation complexity. In this paper, association rules implied in the labels are explored to implement a fuzzy-rough feature selection method for multi-label datasets. Specifically, in order to reduce the scale of label and avoid the label overlapping phenomenon, the association rules between labels make the combination of labels collapse to a set of sub-labels. Then each set of sub-labels is regarded as a unique class during the following course of fuzzy-rough feature selection. Empirical results suggest that the quality of the selected features can be improved by the proposed approach compared to the alternative multi-label feature selection algorithms.