Distributed representation learning with skip-gram model for trained random forests

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....

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Main Authors: MA, Chao, WANG, Tianjun, ZHANG, Le, CAO, Zhiguang, HUANG, Yue, DING, Xinghao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/8220
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spelling sg-smu-ink.sis_research-92232023-10-13T09:18:03Z Distributed representation learning with skip-gram model for trained random forests MA, Chao WANG, Tianjun ZHANG, Le CAO, Zhiguang HUANG, Yue DING, Xinghao 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. 2023-09-28T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/8220 info:doi/10.1016/j.neucom.2023.126434 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Distributed representation learning random forest co-occurrence relationship Skip-Gram feature hashing Databases and Information Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Distributed representation learning
random forest
co-occurrence relationship
Skip-Gram
feature hashing
Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle Distributed representation learning
random forest
co-occurrence relationship
Skip-Gram
feature hashing
Databases and Information Systems
Graphics and Human Computer Interfaces
MA, Chao
WANG, Tianjun
ZHANG, Le
CAO, Zhiguang
HUANG, Yue
DING, Xinghao
Distributed representation learning with skip-gram model for trained random forests
description 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.
format text
author MA, Chao
WANG, Tianjun
ZHANG, Le
CAO, Zhiguang
HUANG, Yue
DING, Xinghao
author_facet MA, Chao
WANG, Tianjun
ZHANG, Le
CAO, Zhiguang
HUANG, Yue
DING, Xinghao
author_sort MA, Chao
title Distributed representation learning with skip-gram model for trained random forests
title_short Distributed representation learning with skip-gram model for trained random forests
title_full Distributed representation learning with skip-gram model for trained random forests
title_fullStr Distributed representation learning with skip-gram model for trained random forests
title_full_unstemmed Distributed representation learning with skip-gram model for trained random forests
title_sort distributed representation learning with skip-gram model for trained random forests
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/8220
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