[1]ZHANG Yuanjian,ZHAO Tianna,MIAO Duoqian.Granule-based label enhancement in label distribution learning[J].CAAI Transactions on Intelligent Systems,2023,18(2):390-398.[doi:10.11992/tis.202208015]
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Granule-based label enhancement in label distribution learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 390-398 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2023-05-05
Title:
Granule-based label enhancement in label distribution learning
Author(s):
ZHANG Yuanjian1;  ZHAO Tianna2;  MIAO Duoqian2
1. China UnionPay Co. Ltd, Shanghai 201201, China;
2. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Keywords:
granular computing; label distribution learning; label enhancement; multi-label; uncertainty; local label correlation; clustering; topology
CLC:
TP391
DOI:
10.11992/tis.202208015
Abstract:
Label distribution learning can effectively deal with multilabel learning tasks. However, the construction of a classifier is based on the premise of obtaining large-scale labels with strong supervision information, which is difficult to be satisfied in many applications. An alternative solution is to mine the importance of implicit numerical labels from the traditional logical form of annotation through label enhancement. Existing label enhancement methods mainly assume that the enhanced label must maintain the relevance of the original logical label in all instances, which fails to preserve local label correlation. This paper proposes a granular-based label enhancement distribution model applicable to label distribution learning, considering the methodology of granular computing. The method constructs information granules with local correlation semantics by employing k-means clustering and completes the labeling transformation of instances in granules on the graph according to the characteristics of logical labeling and the topological properties of attribute space at the abstract level of granules. Finally, the obtained label distribution is fused at the instance level, obtaining the numerical label describing the importance of the whole data set. Extensive studies have shown that the proposed model significantly improves the accuracy of multilabel learning.
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