Abstract
Feature Select Validation (FSV) is a widely used validation method for data comparison. FSV provides a quantitative standard to describe the similarity between two sets of data. in this paper, the application of the FSV technique is extended to data optimization. the raw data obtained from simulations or measurements are often non-ideal for further processing. Several techniques, such as data perturbation, can be used to improve the data quality in certain aspects. However, after modifications the new data could be very different to the original one. using FSV as an objective function for the optimization process is discussed in this paper, in an example of causality enforcement, to ensure the enforced casual data has the minimum deviations from the original data. the results demonstrate that the proposed approach in this paper is effective for data modification and optimization. ©2010 IEEE.
Recommended Citation
S. Pan et al., "Applying Feature Selective Validation (FSV) as an Objective Function for Data Optimization," IEEE International Symposium on Electromagnetic Compatibility, pp. 718 - 721, article no. 5711366, Institute of Electrical and Electronics Engineers, Dec 2010.
The definitive version is available at https://doi.org/10.1109/ISEMC.2010.5711366
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Causality check; Causality enforcement; Data optimization; Data perturbation; Feature selective validation (FSV)
International Standard Book Number (ISBN)
978-142446305-3
International Standard Serial Number (ISSN)
1077-4076
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2010
