Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

DOI

10.1080/18756891.2012.685292How to use a DOI?

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

© 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/).

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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  -