Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression
Summary :
Multi-task learning is an important area of machine learning that tries to learn multiple tasks simultaneously to improve the accuracy of each individual task. We propose a new tree-based ensemble multi-task learning method for classification and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MT-ExtraTrees is able to share data between tasks minimizing negative transfer while keeping the ability to learn non-linear solutions and to scale well to large datasets.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.6 pp.1677-1681
- Publication Date
- 2014/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.1677
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Jaak SIMM
Tallinn University of Technology
Ildefons MAGRANS DE ABRIL
Vrije Universiteit Brussel
Masashi SUGIYAMA
Tokyo Institute of Technology
Keyword
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Jaak SIMM, Ildefons MAGRANS DE ABRIL, Masashi SUGIYAMA, "Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 6, pp. 1677-1681, June 2014, doi: 10.1587/transinf.E97.D.1677.
Abstract: Multi-task learning is an important area of machine learning that tries to learn multiple tasks simultaneously to improve the accuracy of each individual task. We propose a new tree-based ensemble multi-task learning method for classification and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MT-ExtraTrees is able to share data between tasks minimizing negative transfer while keeping the ability to learn non-linear solutions and to scale well to large datasets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1677/_p
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@ARTICLE{e97-d_6_1677,
author={Jaak SIMM, Ildefons MAGRANS DE ABRIL, Masashi SUGIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression},
year={2014},
volume={E97-D},
number={6},
pages={1677-1681},
abstract={Multi-task learning is an important area of machine learning that tries to learn multiple tasks simultaneously to improve the accuracy of each individual task. We propose a new tree-based ensemble multi-task learning method for classification and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MT-ExtraTrees is able to share data between tasks minimizing negative transfer while keeping the ability to learn non-linear solutions and to scale well to large datasets.},
keywords={},
doi={10.1587/transinf.E97.D.1677},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression
T2 - IEICE TRANSACTIONS on Information
SP - 1677
EP - 1681
AU - Jaak SIMM
AU - Ildefons MAGRANS DE ABRIL
AU - Masashi SUGIYAMA
PY - 2014
DO - 10.1587/transinf.E97.D.1677
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2014
AB - Multi-task learning is an important area of machine learning that tries to learn multiple tasks simultaneously to improve the accuracy of each individual task. We propose a new tree-based ensemble multi-task learning method for classification and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MT-ExtraTrees is able to share data between tasks minimizing negative transfer while keeping the ability to learn non-linear solutions and to scale well to large datasets.
ER -
English
