Domain based classification
Machine Learning
2018-08-14 v2 Machine Learning
Abstract
The majority of traditional classification ru les minimizing the expected probability of error (0-1 loss) are inappropriate if the class probability distributions are ill-defined or impossible to estimate. We argue that in such cases class domains should be used instead of class distributions or densities to construct a reliable decision function. Proposals are presented for some evaluation criteria and classifier learning schemes, illustrated by an example.
Cite
@article{arxiv.1601.04530,
title = {Domain based classification},
author = {Robert P. W. Duin and Elzbieta Pekalska},
journal= {arXiv preprint arXiv:1601.04530},
year = {2018}
}
Comments
8 pages, unpublished paper written in 2005, discussing a significant, still almost not studied problem. missing reference links corrected