Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers
- 10.1080/18756891.2012.685292How to use a DOI?
- Genetic Fuzzy Systems, Interval Valued Data, Imbalanced Classification, Low Quality Data
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.
- © 2017, 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 - JOUR AU - AnaM. Palacios AU - Luciano Sánchez AU - Inés Couso PY - 2012 DA - 2012/04/01 TI - Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers JO - International Journal of Computational Intelligence Systems SP - 276 EP - 296 VL - 5 IS - 2 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2012.685292 DO - 10.1080/18756891.2012.685292 ID - Palacios2012 ER -