Comments on “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization” by Eyke Hüllermeier

TitleComments on “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization” by Eyke Hüllermeier
Publication TypeJournal Article
Year of Publication2014
AuthorsSánchez, Luciano
JournalInternational Journal of Approximate Reasoning
Volume55
Number7
Pagination1583 - 1587
ISSN0888-613X
Keywordsclassification, fuzzy data, Imprecise data, Loss functions, machine learning, Regression
Abstract

The paper by Eyke Hüllermeier introduces a new set of techniques for learning models from imprecise data. The removal of the uncertainty in the training instances through the input–output relationship described by the model is also considered. This discussion addresses three points of the paper: extension principle-based models, precedence operators between fuzzy losses and possible connections between data disambiguation and data imputation.

Notes

Special issue: Harnessing the information contained in low-quality data sources

URLhttp://www.sciencedirect.com/science/article/pii/S0888613X14000607
DOI10.1016/j.ijar.2014.04.008