Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression

被引：57

作者：

Simm, Jaak ^{[1
]}

Magrans De Abril, Ildefons ^{[2
]}

Sugiyama, Masashi ^{[3
]}

机构：

[1] Tallinn Univ Technol, Fac Sci, EE-19086 Tallinn, Estonia

[2] Vrije Univ Brussel, Artificial Intelligence Lab, Brussels, Belgium

[3] Tokyo Inst Technol, Dept Comp Sci, Tokyo 1528552, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2014年 / E97D卷 / 06期

关键词：

machine learning; multi-task learning; tree-based methods; ensemble methods;

D O I：

10.1587/transinf.E97.D.1677

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1677 / 1681

页数：5

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