Generalized stochastic orderings applied to the study of performance of machine learning algorithms for low quality data

DOI

10.2991/ifsa-eusflat-15.2015.218How to use a DOI?

Keywords

Regression, classification, loss function, generalized stochastic ordering, set-valued data, low-quality data.

Abstract

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.

Copyright

© 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/).

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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  -