Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies
Joint Authors
Fadlalla, Adam
Munakata, Toshinori
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We consider the generation of stochastic data under constraints where the constraints can be expressed in terms of different parameter sets.
Obviously, the constraints and the generated data must remain the same over each parameter set.
Otherwise, the parameters and/or the generated data would be inconsistent.
We consider how to avoid or detect and then correct such inconsistencies under three proposed classifications: (1) data versus characteristic parameters, (2) macro- versus microconstraint scopes, and (3) intra- versus intervariable relationships.
We propose several strategies and a heuristic for generating consistent stochastic data.
Experimental results show that these strategies and heuristic generate more consistent data than the traditional discard-and-replace methods.
Since generating stochastic data under constraints is a very common practice in many areas, the proposed strategies may have wide-ranging applicability.
American Psychological Association (APA)
Fadlalla, Adam& Munakata, Toshinori. 2014. Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049359
Modern Language Association (MLA)
Fadlalla, Adam& Munakata, Toshinori. Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1049359
American Medical Association (AMA)
Fadlalla, Adam& Munakata, Toshinori. Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049359
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049359