.png){kind=icon}
返回
Fuzzy Multioutput Transfer Learning for Regression
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2021.3083956.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multioutput regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multioutput data and the divergence of data are other factors that impede the development of multioutput regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowledge from a similar domain, provide a solution to the above issues. In this article, a novel fuzzy transfer learning method is proposed to tackle the multioutput regression problems in homogeneous and heterogeneous scenarios. By considering output-input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.
Keyword:
Transfer learning
Task analysis
Correlation
Training data
Predictive models
Data models
Biological system modeling
Domain adaptation
fuzzy rules
machine learning
multioutput regression
transfer learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
机构
引用论文
Transferring knowledge as heuristics in reinforcement learning: A case-based approach强化学习中的启发式知识转移: 一种基于案例的方法
OAAI
OAAI分享收藏Multi-target regression via input space expansion: treating targets as inputs.png)
MACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏A new text categorization technique using distributional clustering and learning logic一种新的基于分布式聚类和学习逻辑的文本分类技术
PREAI
分享收藏Multi-label classification via multi-target regression on data streams基于多目标回归的数据流多标签分类MACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏分享收藏
查看更多内容