title:
 
Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers
publication:
 
IJCIS
volume-issue:   5 - 2
pages:   276 - 296
ISSN:
  1875-6883
DOI:
  doi:10.2991/10.1080/18756891.2012.685292 (how to use a DOI)
author(s):
 
AnaM. Palacios, Luciano Sánchez, Inés Couso
publication date:
 
April 2012
keywords:
 
Genetic Fuzzy Systems, Interval Valued Data, Imbalanced Classification, Low Quality Data
abstract:
 
Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these extended preprocessing algorithms, certain classification systems designed for minimizing the fraction of misclassifications are able to produce knowledge bases that are also adequate under common metrics for imbalanced classification.
copyright:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: