Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints

In this paper, the noise-enhanced detection problem is investigated for the binary hypothesis-testing. The optimal additive noise is determined according to a criterion proposed by DeGroot and Schervish (2011), which aims to minimize the weighted sum of type I and II error probabilities under constr...

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Main Authors: Shujun Liu, Ting Yang, Kui Zhang
Format: Article
Language:English
Published: MDPI AG 2017-06-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/19/6/276
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author Shujun Liu
Ting Yang
Kui Zhang
author_facet Shujun Liu
Ting Yang
Kui Zhang
author_sort Shujun Liu
collection DOAJ
description In this paper, the noise-enhanced detection problem is investigated for the binary hypothesis-testing. The optimal additive noise is determined according to a criterion proposed by DeGroot and Schervish (2011), which aims to minimize the weighted sum of type I and II error probabilities under constraints on type I and II error probabilities. Based on a generic composite hypothesis-testing formulation, the optimal additive noise is obtained. The sufficient conditions are also deduced to verify whether the usage of the additive noise can or cannot improve the detectability of a given detector. In addition, some additional results are obtained according to the specificity of the binary hypothesis-testing, and an algorithm is developed for finding the corresponding optimal noise. Finally, numerical examples are given to verify the theoretical results and proofs of the main theorems are presented in the Appendix.
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spelling doaj.art-69719fc81c6f43019b8ce168d0d91b4c2022-12-22T02:54:42ZengMDPI AGEntropy1099-43002017-06-0119627610.3390/e19060276e19060276Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with ConstraintsShujun Liu0Ting Yang1Kui Zhang2College of Communication Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Communication Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Communication Engineering, Chongqing University, Chongqing 400044, ChinaIn this paper, the noise-enhanced detection problem is investigated for the binary hypothesis-testing. The optimal additive noise is determined according to a criterion proposed by DeGroot and Schervish (2011), which aims to minimize the weighted sum of type I and II error probabilities under constraints on type I and II error probabilities. Based on a generic composite hypothesis-testing formulation, the optimal additive noise is obtained. The sufficient conditions are also deduced to verify whether the usage of the additive noise can or cannot improve the detectability of a given detector. In addition, some additional results are obtained according to the specificity of the binary hypothesis-testing, and an algorithm is developed for finding the corresponding optimal noise. Finally, numerical examples are given to verify the theoretical results and proofs of the main theorems are presented in the Appendix.http://www.mdpi.com/1099-4300/19/6/276noise enhancementhypothesis testingweighted sumerror probability
spellingShingle Shujun Liu
Ting Yang
Kui Zhang
Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints
Entropy
noise enhancement
hypothesis testing
weighted sum
error probability
title Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints
title_full Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints
title_fullStr Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints
title_full_unstemmed Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints
title_short Noise Enhancement for Weighted Sum of Type I and II Error Probabilities with Constraints
title_sort noise enhancement for weighted sum of type i and ii error probabilities with constraints
topic noise enhancement
hypothesis testing
weighted sum
error probability
url http://www.mdpi.com/1099-4300/19/6/276
work_keys_str_mv AT shujunliu noiseenhancementforweightedsumoftypeiandiierrorprobabilitieswithconstraints
AT tingyang noiseenhancementforweightedsumoftypeiandiierrorprobabilitieswithconstraints
AT kuizhang noiseenhancementforweightedsumoftypeiandiierrorprobabilitieswithconstraints