Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1447-1454.DOI: 10.11772/j.issn.1001-9081.2021030516
• Data science and technology • Previous Articles Next Articles
Ying CHEN1, Jiong YU1,2(), Jiaying CHEN2, Xusheng DU2
Received:
2021-04-06
Revised:
2021-06-22
Accepted:
2021-06-22
Online:
2022-06-11
Published:
2022-05-10
Contact:
Jiong YU
About author:
CHEN Ying, born in 1999,M. S. candidate. Her research interestsinclude data mining,machine learning.Supported by:
通讯作者:
于炯
作者简介:
陈颖(1999—),女,湖南娄底人,硕士研究生,主要研究方向:数据挖掘、机器学习基金资助:
CLC Number:
Ying CHEN, Jiong YU, Jiaying CHEN, Xusheng DU. Cross-layer data sharing based multi-task model[J]. Journal of Computer Applications, 2022, 42(5): 1447-1454.
陈颖, 于炯, 陈嘉颖, 杜旭升. 基于交叉层级数据共享的多任务模型[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1447-1454.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030516
数据集 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|
合成数据1 | 1 000 000 | 100 000 | 100 000 |
合成数据2 | 100 000 | 10 000 | 10 000 |
Tab. 1 Synthetic datasets used in experiments
数据集 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|
合成数据1 | 1 000 000 | 100 000 | 100 000 |
合成数据2 | 100 000 | 10 000 | 10 000 |
数据集 | 总样本数 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|---|
UCI census-income | 299 285 | 199 523 | 49 881 | 49 881 |
MovieLens | 100 000 | 70 000 | — | 30 000 |
Tab. 2 Real datasets used in experiments
数据集 | 总样本数 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|---|
UCI census-income | 299 285 | 199 523 | 49 881 | 49 881 |
MovieLens | 100 000 | 70 000 | — | 30 000 |
模型 | Task1-Income | Task2-Marital | MCV-AUC | MCV-F1 | MCV-ACC | ||||
---|---|---|---|---|---|---|---|---|---|
AUC | F1-score | ACC | AUC | F1-score | ACC | ||||
Single-task | 0.932 5 | 0.693 1 | 0.952 0 | 0.970 8 | 0.927 0 | 0.928 3 | — | — | — |
Shared-bottom | 0.904 9 | 0.643 6 | 0.845 1 | 0.974 2 | 0.931 3 | 0.932 7 | 1.879 1 | 1.574 9 | 1.777 8 |
Cross-stitch | 0.929 4 | 0.742 3 | 0.950 5 | 0.984 3 | 0.933 4 | 0.934 5 | 1.913 7 | 1.675 7 | 1.885 0 |
PLE | 0.941 5 | 0.713 9 | 0.950 9 | 0.980 6 | 0.927 2 | 0.929 0 | 1.922 1 | 1.641 1 | 1.879 9 |
MMOE | 0.939 3 | 0.679 0 | 0.948 2 | 0.984 9 | 0.932 5 | 0.933 6 | 1.924 2 | 1.611 5 | 1.881 8 |
CLS-0 | 0.946 1 | 0.753 4 | 0.953 2 | 0.986 0 | 0.933 5 | 0.934 6 | 1.932 1 | 1.687 8 | 1.887 8 |
CLS | 0.946 8 | 0.757 7 | 0.953 3 | 0.988 7 | 0.944 4 | 0.945 8 | 1.935 5 | 1.702 1 | 1.899 1 |
Tab. 3 Experimental results on UCI census-income dataset
模型 | Task1-Income | Task2-Marital | MCV-AUC | MCV-F1 | MCV-ACC | ||||
---|---|---|---|---|---|---|---|---|---|
AUC | F1-score | ACC | AUC | F1-score | ACC | ||||
Single-task | 0.932 5 | 0.693 1 | 0.952 0 | 0.970 8 | 0.927 0 | 0.928 3 | — | — | — |
Shared-bottom | 0.904 9 | 0.643 6 | 0.845 1 | 0.974 2 | 0.931 3 | 0.932 7 | 1.879 1 | 1.574 9 | 1.777 8 |
Cross-stitch | 0.929 4 | 0.742 3 | 0.950 5 | 0.984 3 | 0.933 4 | 0.934 5 | 1.913 7 | 1.675 7 | 1.885 0 |
PLE | 0.941 5 | 0.713 9 | 0.950 9 | 0.980 6 | 0.927 2 | 0.929 0 | 1.922 1 | 1.641 1 | 1.879 9 |
MMOE | 0.939 3 | 0.679 0 | 0.948 2 | 0.984 9 | 0.932 5 | 0.933 6 | 1.924 2 | 1.611 5 | 1.881 8 |
CLS-0 | 0.946 1 | 0.753 4 | 0.953 2 | 0.986 0 | 0.933 5 | 0.934 6 | 1.932 1 | 1.687 8 | 1.887 8 |
CLS | 0.946 8 | 0.757 7 | 0.953 3 | 0.988 7 | 0.944 4 | 0.945 8 | 1.935 5 | 1.702 1 | 1.899 1 |
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