Abstract
The paper proposes a new variant of a decision tree, called an Extreme Learning Tree. It consists of an extremely random tree with non-linear data transformation, and a linear observer that provides predictions based on the leaf index where the data samples fall. The proposed method outperforms linear models on a benchmark dataset, and may be a building block for a future variant of Random Forest.
Original language | English |
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Title of host publication | International Conference on Extreme Learning Machine: Proceedings of ELM-2017 |
Number of pages | 5 |
Place of Publication | Cham |
Publisher | Springer |
Publication date | 17.10.2018 |
Pages | 181-185 |
ISBN (Print) | 978-3-030-01519-0 |
ISBN (Electronic) | 978-3-030-01520-6 |
DOIs | |
Publication status | Published - 17.10.2018 |
MoE publication type | A4 Article in conference proceedings |
Event | 2017 the 8th International Conference on Extreme Learning Machines (ELM) - Yantai, China Duration: 04.10.2017 → 07.10.2017 http://www.ntu.edu.sg/home/egbhuang/elm2017/index.html |
Publication series
Name | Proceedings in Adaptation, Learning and Optimization (PALO) |
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Volume | 10 |
Keywords
- 512 Business and Management
- ELM
- Decision tree
- Randomized methods