Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Classification based on Neighborhood from Datasets with Low Quality Data

Authors
José Manuel Cadenas, Mª Carmen Garrido, Raquel Martínez, Antonio Muñoz-Ledesma
Corresponding Author
José Manuel Cadenas
Available Online June 2015.
DOI
https://doi.org/10.2991/ifsa-eusflat-15.2015.130How to use a DOI?
Keywords
Low quality data, Nearest neighbor, Classification, Fuzzy distance
Abstract
Currently there are not many data mining method available to solve the classification task in datasets with low quality values. In this paper we propose a method of imputation/classification based on neighborhood that can work with nominal and numerical attributes which can contain low quality values. Performing a series of experiments we observe that the method not only is competitive to other similar method when working with datasets without low quality values, but it also obtains robust results when working with datasets with low quality values.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - José Manuel Cadenas
AU  - Mª Carmen Garrido
AU  - Raquel Martínez
AU  - Antonio Muñoz-Ledesma
PY  - 2015/06
DA  - 2015/06
TI  - Classification based on Neighborhood from Datasets with Low Quality Data
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 925
EP  - 932
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.130
DO  - https://doi.org/10.2991/ifsa-eusflat-15.2015.130
ID  - Cadenas2015/06
ER  -