.png){kind=icon}
Return
Hierarchical attribute reduction algorithms for big data using MapReduce
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2014.09.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Attribute reduction is one of the important research issues in rough set theory. Most existing attribute reduction algorithms are now faced with two challenging problems. On one hand, they have seldom taken granular computing into consideration. On the other hand, they still cannot deal with big data. To address these issues, the hierarchical encoded decision table is first defined. The relationships of hierarchical decision tables are then discussed under different levels of granularity. The parallel computations of the equivalence classes and the attribute significance are further designed for attribute reduction. Finally, hierarchical attribute reduction algorithms are proposed in data and task parallel using MapReduce. Experimental results demonstrate that the proposed algorithms can scale well and efficiently process big data. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Hierarchical attribute reduction
Granular computing
Data and task parallelism
MapReduce
Big data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
View more organizations
Cited Papers
Acquisition of hierarchy-structured probabilistic decision tables and rules from data.png)
EXPERT SYSTEMS
EXPERT SYSTEMSIF2.3
PREAI
ShareSaveShareSaveAuthor response: Method for identification of condition-associated public antigen receptor sequences
OAAI
IF0
OAAIShareSaveShareSaveShareSave
View more