.png){kind=icon}
返回
Distributed representation learning with skip-gram model for trained random forests
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2023.126434.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The random forest family has been extensively studied due to its wide applications in machine learning and data analytics. However, the representation abilities of forests have not been explored yet. The existing forest representation is mainly based on feature hashing on the indices of leaf nodes. Feature hashing typically disregards the information from tree structures, i.e., the relationships between leaf nodes. Furthermore, the visualisation abilities of feature hashing are limited. On the contrary, the Skip-Gram model has been widely explored in word and node embedding due to its excellent representation ability. This paper proposes distributed representation learning for trained forests (DRL-TF) to extract co-occurrence relationships of samples and tree structures, and further boost the representation abilities of the trained forest using the Skip-Gram model. The experimental results demonstrate that the proposed DRL-TF outperforms the challenging baselines. To the best of the authors' knowledge, the visualisation by DRL-TF is the first tool to analyse the trained forests. The code is available at: https://github.com/machao199271/ DRL-TF.
Keyword:
Distributed representation learning
random forest
co-occurrence relationship
Skip-Gram
feature hashing
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Dual coordinate descent methods for logistic regression and maximum entropy modelslogistic回归和最大熵模型的双坐标下降方法.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏The SO2 camera: A simple, fast and cheap method for ground‐based imaging of SO2 in volcanic plumes
OAAI
OAAI分享收藏Highly efficient electrocatalytic hydrogen production by nickel promoted molybdenum sulfide microspheres catalysts镍促进硫化钼微球催化剂的高效电催化制氢RSC Advances
RSC AdvancesIF0
PREAI
分享收藏A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications图嵌入的综合调查: 问题，技术和应用OAAI
OAAI分享收藏Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏
查看更多内容