A fusion method for multi-valued data
Machine Learning
2021-01-26 v1 Artificial Intelligence
Neural and Evolutionary Computing
Abstract
In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.
Cite
@article{arxiv.2101.10115,
title = {A fusion method for multi-valued data},
author = {Martin Papčo and Iosu Rodríguez-Martínez and Javier Fumanal-Idocin and Abdulrahman H. Altalhi and Humberto Bustince},
journal= {arXiv preprint arXiv:2101.10115},
year = {2021}
}