A locally weighted learning method based on a data gravitation model for multi-target regression
- Oscar Reyes1, Alberto Cano2, Habib M. Fardoun3, Sebastián Ventura1, 31 Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain2 Department of Computer Science, Virginia Commonwealth University, United States3 Department of Information Systems, King Abdulaziz University, Saudi Arabia Kingdom
- 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 -