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Parallel Attribute Reduction Algorithm for Complex Heterogeneous Data Using MapReduce
Joint Authors
Yue, Dong
Cao, Jie
Zhang, Tengfei
Ma, Fumin
Peng, Chen
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-27
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Parallel attribute reduction is one of the most important topics in current research on rough set theory.
Although some parallel algorithms were well documented, most of them are still faced with some challenges for effectively dealing with the complex heterogeneous data including categorical and numerical attributes.
Aiming at this problem, a novel attribute reduction algorithm based on neighborhood multigranulation rough sets was developed to process the massive heterogeneous data in the parallel way.
The MapReduce-based parallelization method for attribute reduction was proposed in the framework of neighborhood multigranulation rough sets.
To improve the reduction efficiency, the hashing Map/Reduce functions were designed to speed up the positive region calculation.
Thereafter, a quick parallel attribute reduction algorithm using MapReduce was developed.
The effectiveness and superiority of this parallel algorithm were demonstrated by theoretical analysis and comparison experiments.
American Psychological Association (APA)
Zhang, Tengfei& Ma, Fumin& Cao, Jie& Peng, Chen& Yue, Dong. 2018. Parallel Attribute Reduction Algorithm for Complex Heterogeneous Data Using MapReduce. Complexity،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1136117
Modern Language Association (MLA)
Zhang, Tengfei…[et al.]. Parallel Attribute Reduction Algorithm for Complex Heterogeneous Data Using MapReduce. Complexity No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1136117
American Medical Association (AMA)
Zhang, Tengfei& Ma, Fumin& Cao, Jie& Peng, Chen& Yue, Dong. Parallel Attribute Reduction Algorithm for Complex Heterogeneous Data Using MapReduce. Complexity. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1136117
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136117