Classification based on Neighborhood from Datasets with Low Quality Data
José Manuel Cadenas, Mª Carmen Garrido, Raquel Martínez, Antonio Muñoz-Ledesma
José Manuel Cadenas
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.130How to use a DOI?
- Low quality data, Nearest neighbor, Classification, Fuzzy distance
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.
- © 2015, 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 - 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 - 10.2991/ifsa-eusflat-15.2015.130 ID - Cadenas2015/06 ER -