Volume 11, Issue 1, 2018, Pages 1307 - 1321
Dealing with Missing Data using a Selection Algorithm on Rough Sets
Received 28 February 2018, Accepted 26 June 2018, Available Online 11 July 2018.
- DOI
- 10.2991/ijcis.11.1.97How to use a DOI?
- Keywords
- Categorical; Imputation; Missing Values; Rough Sets
- Abstract
This paper discusses the so-called missing data problem, i.e. the problem of imputing missing values in information systems. A new algorithm, called the ARSI algorithm, is proposed to address the imputation problem of missing values on categorical databases using the framework of rough set theory. This algorithm can be seen as a refinement of the ROUSTIDA algorithm and combines the approach of a generalized non-symmetric similarity relation with a generalized discernibility matrix to predict the missing values on incomplete information systems. Computational experiments show that the proposed algorithm is as efficient and competitive as other imputation algorithms.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - Jonathan Prieto-Cubides AU - Camilo Argoty PY - 2018 DA - 2018/07/11 TI - Dealing with Missing Data using a Selection Algorithm on Rough Sets JO - International Journal of Computational Intelligence Systems SP - 1307 EP - 1321 VL - 11 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.11.1.97 DO - 10.2991/ijcis.11.1.97 ID - Prieto-Cubides2018 ER -