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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Trường Đại học Đà Lạt
2014
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oai:scholar.dlu.edu.vn:DLU123456789-375232023-10-27T14:42:15Z Parallel algorithms of random forests for classifying very large datasets Do, Thanh Nghi Pham, Nguyen Khang Random forest Decision tree Bagging Boosting 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. 2014-06-05T07:42:11Z 2014-06-05T07:42:11Z 2013 Article https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/37523 en Tạp chí Khoa học Đại học Đà Lạt, số 6;tr. 21-31 application/pdf Trường Đại học Đà Lạt |
| institution |
Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Random forest Decision tree Bagging Boosting |
| spellingShingle |
Random forest Decision tree Bagging Boosting Do, Thanh Nghi Pham, Nguyen Khang Parallel algorithms of random forests for classifying very large datasets |
| description |
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. |
| format |
Article |
| author |
Do, Thanh Nghi Pham, Nguyen Khang |
| author_facet |
Do, Thanh Nghi Pham, Nguyen Khang |
| author_sort |
Do, Thanh Nghi |
| title |
Parallel algorithms of random forests for classifying very large datasets |
| title_short |
Parallel algorithms of random forests for classifying very large datasets |
| title_full |
Parallel algorithms of random forests for classifying very large datasets |
| title_fullStr |
Parallel algorithms of random forests for classifying very large datasets |
| title_full_unstemmed |
Parallel algorithms of random forests for classifying very large datasets |
| title_sort |
parallel algorithms of random forests for classifying very large datasets |
| publisher |
Trường Đại học Đà Lạt |
| publishDate |
2014 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/37523 |
| _version_ |
1781718939765571584 |