Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

Tianna Zhao, Yuanjian Zhang and Duoqian Miao
Additional contact information
Tianna Zhao: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Yuanjian Zhang: China UnionPay Co., Ltd., Shanghai 201201, China
Duoqian Miao: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China

Mathematics, 2022, vol. 10, issue 11, 1-21

Abstract: Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is O n 2 m k , where n , m , and k denote the unseen instances count, label count, and average label-specific feature size, respectively.

Keywords: multi-label classification; uncertainty; three-way decisions; intuitionistic fuzzy number; instance selection principle; label enhancement (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/11/1847/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/11/1847/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:11:p:1847-:d:826031

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().