Generalized stochastic orderings applied to the study of performance of machine learning algorithms for low quality data
- 10.2991/ifsa-eusflat-15.2015.218How to use a DOI?
- Regression, classification, loss function, generalized stochastic ordering, set-valued data, low-quality data.
Usually, the expected loss minimization criterion is used in order to look for the optimal model that expresses a certain response variable as a function of a collection of attributes. We generalize this criterion, in order to be able to deal also with those situations where a numerical loss function makes no sense or is not provided by the expert. In a first stage, we consider the new framework in standard situations, where both the collection of attributes and the response variables are observed with precision. In a second one, we assume that we are just provided with imprecise information about them (in terms of set-valued data sets). We cast some comparison criteria from the recent literature on learning methods from low-quality data as particular cases of our general approach.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Inés Couso AU - Luciano Sánchez PY - 2015/06 DA - 2015/06 TI - Generalized stochastic orderings applied to the study of performance of machine learning algorithms for low quality data BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1534 EP - 1541 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.218 DO - 10.2991/ifsa-eusflat-15.2015.218 ID - Couso2015/06 ER -