Probabilistic behavior in perceiving the environment and take critical decisions have
an inevitable role in human life. A decision is concerned with a choice among the
available alternatives and is always subject to unknown elements concerning the
future. The lack of complete data, insufficient scientific, behavioral, and industry
development and of course defects in measurement methods, affect the reliability of an
action’s outcome. Thus, having a proper estimation of this reliability or uncertainty
could be very advantageous particularly when an individual or generally a subject
is faced with a high risk. With the fact that there are always uncertainty elements
whose values are unknown and these enter into a processes through multiple sources,
it has been a primary challenge to design an efficient representation of confidence
objectively. With the aim of addressing this problem, a variety of researches have
been conducted to introduce frameworks in metrology of uncertainty quantification
that are comprehensive enough and have transferability into different areas. Moreover,
it’s also a challenging task to define a proper index that reflects more aspects of the
problem and measurement process.
With significant advances in Artificial Intelligence in the past decade, one of the
key elements, in order to ease human life by giving more control to machines, is to
heed the uncertainty estimation for a prediction. With a focus on measurement aspects, this thesis attends to demonstrate how a
different measurement index affects the quality of evaluated predictive uncertainty
of neural networks. Finally, we propose a novel index that shows uncertainty values
with the same or higher quality than existing methods which emphasizes the benefits
of having a proper measurement index in managing the risk of the outcome from a
classification model.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44489 |
| Date | 10 January 2023 |
| Creators | Amiri, Mohammad Hadi |
| Contributors | Al Osman, Hussein, Shirmohammadi, Shervin |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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