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On the Application of the Supervised Machine Learning to Trustworthiness Assessment

Hauke, Sascha and Biedermann, Sebastian and Mühlhäuser, Max and Heider, Dominik (2013):
On the Application of the Supervised Machine Learning to Trustworthiness Assessment.
In: 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), ISBN 9781479914449,
[Conference or Workshop Item]

Abstract

State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world data set, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.

Item Type: Conference or Workshop Item
Erschienen: 2013
Creators: Hauke, Sascha and Biedermann, Sebastian and Mühlhäuser, Max and Heider, Dominik
Title: On the Application of the Supervised Machine Learning to Trustworthiness Assessment
Language: English
Abstract:

State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world data set, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.

Journal or Publication Title: Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-13)
ISBN: 9781479914449
Divisions: 20 Department of Computer Science > Telecooperation
20 Department of Computer Science
Event Title: 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Date Deposited: 20 Apr 2015 15:41
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