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Imputation of Missing Data using Fuzzy-Rough Hybridization

Pallab Kumar Dey1

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-01 , Page no. 226-231, Jan-2019

Online published on Jan 20, 2019

Copyright © Pallab Kumar Dey . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Pallab Kumar Dey, “Imputation of Missing Data using Fuzzy-Rough Hybridization,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.226-231, 2019.

MLA Style Citation: Pallab Kumar Dey "Imputation of Missing Data using Fuzzy-Rough Hybridization." International Journal of Computer Sciences and Engineering 07.01 (2019): 226-231.

APA Style Citation: Pallab Kumar Dey, (2019). Imputation of Missing Data using Fuzzy-Rough Hybridization. International Journal of Computer Sciences and Engineering, 07(01), 226-231.

BibTex Style Citation:
@article{Dey_2019,
author = {Pallab Kumar Dey},
title = {Imputation of Missing Data using Fuzzy-Rough Hybridization},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {07},
Issue = {01},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {226-231},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=623},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=623
TI - Imputation of Missing Data using Fuzzy-Rough Hybridization
T2 - International Journal of Computer Sciences and Engineering
AU - Pallab Kumar Dey
PY - 2019
DA - 2019/01/20
PB - IJCSE, Indore, INDIA
SP - 226-231
IS - 01
VL - 07
SN - 2347-2693
ER -

           

Abstract

Missing data imputation has a significant impact in data mining task. Data mining algorithms cannot be executed effectively due to missing attribute values. Improper handle of missing values affects the data mining and classification accuracy. Imputation based preprocessing approach is very effective technique for handling missing value. In this paper most similar object used to impute missing value. For searching similar object core attributes have to give highest priority after that reduct attributes. In the proposed method to fill missing value concept of core and reduct attributes has been used. Rough set is most suitable to handle discrete data. Fuzzy set can handle continuous data in a better way. Hybridization methodology like fuzzy-rough set are more powerful to deal with imprecision and uncertainty for discrete as well as continuous data. Detail study has been given to impute missing value. Fuzzy rough set based fuzz- rough core reduct based (FRCRB) algorithm has been proposed for missing value imputation.

Key-Words / Index Term

Missing value, Imputation, Rough set, Fuzzy set and fuzzy-rough set, data analysis.

References

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