Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
| Authors | Jaak Simm, Ildefons Magrans de Abril, Masashi Sugiyama |
| Journal/Conference Name | IEICE Transactions |
| Paper Category | Other |
| Paper 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 classication and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MTExtraTrees 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. |
| Date of publication | 2014 |
| Code Programming Language | R |
| Comment |
Copyright Researcher 2022