.png){kind=icon}
.png){kind=icon}
Return
Dominance relation-based feature selection for interval-valued multi-label ordered information system
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2025.126898.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label learning addresses situations where a instance is linked to several labels. Existing multi-label feature selection has mainly addressed single-valued problems, while research on attribute reduction for interval- valued multi-label systems has yet to be reported. And explore how to apply the dominance principle to interval-valued multi-label ordered data is a promising area for future research. In this article, anew feature selection method was introduced, aiming to identify amore relevant and compact subset of features by incorporating label correlations and the dominance principle. First we combine multi-label learning with interval-valued information systems and design a new information system. Second, to make knowledge representation simpler, we discuss the dominance principle of interval-valued multi-label information systems. On this basis, we present a novel method for generating reduction information for each label and introduce a label correlation learning approach that exploits the overlap of this reduction information. Subsequently, an innovative feature selection algorithm utilizes dominance-based rough set is developed to efficiently filter out redundant features in the feature space. Finally, extensive experiments on nine multi-label datasets were performed, and the results confirm that the proposed algorithms surpass six state-of-the-art methods in performance and exhibit robustness.
Keywords:
Multi-label learning
Interval value
Dominance-based rough set approach
Feature selection
Ordered information system
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
View more organizations