Parallel algorithms of random forests for classifying very large datasets
The random forests algorithm proposed by Breiman is an ensemble-based approach with very high accuracy. The learning and classification tasks of a set of decision trees take a lot of time, make it intractable when dealing with ve ry large datasets. There is a need to scale up the random forests a...
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| Autors principals: | , |
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| Format: | Article |
| Idioma: | English |
| Publicat: |
Trường Đại học Đà Lạt
2014
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| Matèries: | |
| Accés en línia: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/37523 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Sumari: | The random forests algorithm proposed by Breiman is an ensemble-based approach
with very high accuracy. The learning and classification tasks of a set of decision trees
take a lot of time, make it intractable when dealing with ve ry large datasets. There is a
need to scale up the random forests algorithm to handle massive datasets. We propose
parallel algorithms of random forests to take into account the benefits of Grids
computing. These algorithms improve training and classification time compared with the
original ones. The experimental results on large datasets including Forest cover type,
KDD Cup 1999, Connect-4 from the UCI data repository showed that the training and
classification time of parallel algorithms are significantly reduced. |
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