Associated multi-label fuzzy-rough feature selection

Qu, Yanpeng, Rong, Yu, Deng, Ansheng and Yang, Longzhi (2017) Associated multi-label fuzzy-rough feature selection. In: 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). IEEE, Piscataway. ISBN 978-1-5090-4918-9

Full text not available from this repository. (Request a copy)
Official URL:


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.

Item Type: Book Section
Uncontrolled Keywords: Multi-label feature selection, Association rule, Fuzzy-rough sets
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 24 Oct 2017 08:29
Last Modified: 24 Oct 2017 08:29

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics

Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence