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Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (2): 44-55    DOI: 10.11925/infotech.2096-3467.2022.1278
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Label Distribution Learning Based on Hierarchical Tag Structure
Liu Kan1(),You Meilin2,Wei Lanxi1
1School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2China Mobile Communications Group Sichuan Co. Ltd., Chengdu 610041, China
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Abstract  

[Objective] This paper focuses on the complex hierarchical relationship between tokens in label distribution learning. It enhance performance by adding the hierarchical tag structure to the label distribution learning model.[Methods] We proposed a hierarchy-based label distribution learning algorithm (H-LDL), which used conditional probability to describe the extensive and intensive tag structural relationship. We also adjusted the exact distribution of each level by the function of hierarchical weighted loss and its optimization strategy. [Results] We examined the new model on two public datasets. The Euclidean, Squared, and K-L scores decreased by 3.99%, 1.07%, and 3.10% on BU_3DFE dataset compared to the baseline model, while Intersec and Fidelity improved by 4.24% and 0.67%. On COMP dataset, the Euclidean decreased by 0.48%, but the Squared and K-L showed no significant decrease, while Intersect and Fidelity metrics increased by 0.45% and 0.02%. [Limitations] We only included two hierarchical relationships in the new model. Further research is needed for more complex hierarchical relationships. [Conclusions] A hierarchical label structure effectively improves the performance of label distribution learning.
Key words： Hierarchical Structure      Label Distribution Learning      Hierarchical Tag      Conditional Probability     
Received: 02 December 2022      Published: 30 March 2023
ZTFLH:  TP391  
  G35  
Fund:National Natural Science Foundation of China(72174156)
Corresponding Authors: Liu Kan，ORCID：0000-0001-9339-7315，E-mail： liukan@zuel.edu.cn。   
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Kan Liu
Meilin You
Lanxi Wei

Cite this article:

Liu Kan, You Meilin, Wei Lanxi. Label Distribution Learning Based on Hierarchical Tag Structure. Data Analysis and Knowledge Discovery, 2024, 8(2): 44-55.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1278     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I2/44

Illustration of Single-Label， Multi-Label and Label Distribution
The Flow Chart of Label Distribution Learning Algorithm Based on Hierarchical Structure
Network Architecture of Label Distribution Learning Algorithm Based on Hierarchical Structure
指标 公式
Euclidean↓ d ( D , D * ) = ∑ i = 1 c ( d i - d i * ) 2
Squared↓ d ( D , D * ) = ∑ i = 1 c ( d i - d i * ) 2
K-L↓ d ( D , D * ) = ∑ i = 1 c d i l n d i d i *
Intersec↑ d ( D , D * ) = ∑ i = 1 c m i n ( d i , d i * )
Fidelity↑ d ( D , D * ) = ∑ i = 1 c d i d i *
Evaluation Index of Label Distribution Learning Algorithm
An Example of the Movie Review Hierarchy Tag Data
数据集 样本数 特征数 粗层次标签数 细层次标签数
BU_3DFE 2 500 243 3 6
COPM 7 755 1 869 3 5
Details of BU_3DFE and COPM Dataset
实验编号 迭代轮次 粗层次权重 w 1 细层次权重 w 2 学习率
1 0<epoch≤150 0.90 0.10 0.020 0
150<epoch≤350 0.50 0.50 0.002 0
350<epoch≤500 0.10 0.90 0.000 2
2 0<epoch≤150 0.60 0.40 0.050 0
150<epoch≤350 0.50 0.50 0.025 0
350<epoch≤500 0.20 0.80 0.002 0
3 0<epoch≤150 0.65 0.35 0.005 0
150<epoch≤350 0.50 0.50 0.002 0
350<epoch≤500 0.35 0.65 0.000 5
4 0<epoch≤150 0.75 0.25 0.005 0
150<epoch≤350 0.50 0.50 0.002 0
350<epoch≤500 0.25 0.75 0.000 5
Parameters of the Four Groups of Experiments
迭代轮次 粗层次权重 w 1 细层次权重 w 2 学习率
0<epoch≤150 0.75 0.25 0.005 0
150<epoch≤350 0.50 0.50 0.002 5
350<epoch≤500 0.20 0.80 0.000 5
Weight Setting During Training
层次 Euclidean↓ Squared↓ K-L↓ Intersec↑ Fidelity↑
粗层次 0.121 3 0.023 5 0.050 6 0.907 0 0.989 7
细层次 0.129 7 0.022 6 0.055 8 0.877 5 0.986 1
Experimental Results on BU_3DFE Dataset
层次 Euclidean↓ Squared↓ K-L↓ Intersec↑ Fidelity↑
粗层次 0.206 1 0.062 5 0.096 1 0.838 1 0.975 8
细层次 0.169 5 0.039 8 0.109 2 0.833 4 0.972 7
Experimental Results on COPM Dataset
算法 Euclidean↓ Squared↓ K-L↓ Intersec↑ Fidelity↑
AA-BP 0.169 6 0.033 3 0.087 5 0.835 1 0.979 1
AA-KNN 0.177 9 0.040 0 0.099 4 0.833 5 0.975 5
CPNN 0.171 1 0.034 9 0.087 5 0.833 9 0.979 4
IIS-LLD 0.170 4 0.034 6 0.086 8 0.833 9 0.979 4
PT-SVM 0.180 6 0.037 0 0.093 5 0.822 0 0.977 7
H-LDL 0.129 7 0.022 6 0.055 8 0.877 5 0.986 1
Baseline Comparison of BU_3DFE
算法 Euclidean↓ Squared↓ K-L↓ Intersec↑ Fidelity↑
AA-BP 0.174 3 0.038 5 0.107 1 0.828 9 0.972 5
AA-KNN 0.180 0 0.042 2 0.115 1 0.822 7 0.970 3
CPNN 0.203 8 0.053 6 0.147 1 0.802 2 0.962 9
IIS-LDL 0.207 6 0.052 6 0.132 2 0.802 5 0.966 2
PT-SVM 0.375 0 0.146 3 0.380 9 0.630 9 0.899 8
H-LDL 0.169 5 0.039 8 0.109 2 0.833 4 0.972 7
Baseline Comparison of COPM
Analysis of the Role of Hierarchical Transformation Layer on BU_3DFE Dataset
Analysis of the Role of Hierarchical Transformation Layer on COPM Dataset
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