A locally weighted learning method based on a data gravitation model for multi-target regression
- https://doi.org/10.2991/ijcis.11.1.22How to use a DOI?
- Multi-Target Regression, Locally Weighted Regression, Data Gravitation Approach
Locally weighted regression allows to adjust the regression models to nearby data of a query example. In this paper, a locally weighted regression method for the multi-target regression problem is proposed. A novel way of weighting data based on a data gravitation-based approach is presented. The process of weighting data does not need to decompose the multi-target data into several single-target problems. This weighted regression method can be used with any multi-target regressor as a local method to provide the target vector of a query example. The proposed method was assessed on the largest collection of multi-target regression datasets publicly available. The experimental stage showed that the performance of multi-target regressors can be significantly improved by means of fitting the models to local training data.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Cite this article
TY - JOUR AU - Oscar Reyes AU - Alberto Cano AU - Habib M. Fardoun AU - Sebastián Ventura PY - 2018 DA - 2018/01 TI - A locally weighted learning method based on a data gravitation model for multi-target regression JO - International Journal of Computational Intelligence Systems SP - 282 EP - 295 VL - 11 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.11.1.22 DO - https://doi.org/10.2991/ijcis.11.1.22 ID - Reyes2018 ER -