Trustworthiness and the importance of graph structure

We begin by giving a comprehensive literature review that ties together many fields which have heretofore remained separate. We comment on the approaches from each field and show which algorithms are similar and which are different. Then, starting from a concrete task, we extend traditional trustworthiness algorithms to deal with the more complex situation of multiclass list-valued trustworthiness. In addition, we introduce a learned predictive method based on standard classification algorithms. In the last section, we explore the theory of trustworthiness and begin to make progress towards charting the space of all trustworthiness graphs. We address the commonly underestimated importance of the structure of a trust- worthiness graph, and define a space in which to work as well as defining the solvability of a trustworthiness graph. Finally, we provide recommendations for future work.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Mayhew, Stephen

Contributors dc:contributor

• Roth, Dan

• Trustworthiness
• data fusion
• crowdsourcing
• machine learning
• natural language processing

Statement dc:rights

• Copyright 2014 Stephen Mayhew

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/49620

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/49620