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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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 |
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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 |
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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. |
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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 |
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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 |
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Distributed representation learning with skip-gram model for trained random forests |
title_sort |
distributed representation learning with skip-gram model for trained random forests |
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Institutional Knowledge at Singapore Management University |
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2023 |
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https://ink.library.smu.edu.sg/sis_research/8220 |
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