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Computers 2018, 7(3), 44; https://doi.org/10.3390/computers7030044

Parallel Computation of Rough Set Approximations in Information Systems with Missing Decision Data

1
Information Science and Control Engineering, Nagaoka University of Technology, Nagaoka 9402137, Japan
2
Human Machine Interaction Laboratory, UET, Vietnam National University, Hanoi 10000, Vietnam
*
Author to whom correspondence should be addressed.
Received: 21 July 2018 / Revised: 12 August 2018 / Accepted: 16 August 2018 / Published: 20 August 2018
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Abstract

The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition data, but very few have accounted for missing decision data, especially in enlarging datasets. One of the approaches for dealing with missing data in condition attributes is named twofold rough approximations. The paper aims to extend the approach to deal with missing data in the decision attribute. In addition, computing twofold rough approximations is very intensive, thus the approach is not suitable when input datasets are large. We propose parallel algorithms to compute twofold rough approximations in large-scale datasets. Our method is based on MapReduce, a distributed programming model for processing large-scale data. We introduce the original sequential algorithm first and then the parallel version is introduced. Comparison between the two approaches through experiments shows that our proposed parallel algorithms are suitable for and perform efficiently on large-scale datasets that have missing data in condition and decision attributes. View Full-Text
Keywords: rough set; rough approximation; mapreduce; twofold rough approximation; missing decision data; missing condition data rough set; rough approximation; mapreduce; twofold rough approximation; missing decision data; missing condition data
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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. (CC BY 4.0).
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Cao, T.; Yamada, K.; Unehara, M.; Suzuki, I.; Nguyen, D.V. Parallel Computation of Rough Set Approximations in Information Systems with Missing Decision Data. Computers 2018, 7, 44.

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