Elicitation of Non-Linearity from Expert Drawing
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Author
Date
2019-06-17
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence
Mcode
SCI3044
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
57
Series
Abstract
Machine learning methods do not perform very well with little data because there is not enough information to learn. The choice is to either obtain more data or elicit knowledge from an expert. Obtaining more data might be infeasible because of the associated cost or required time. In such cases, we opt for expert knowledge elicitation. Current expert knowledge elicitation methods either query the user for data points or regarding the relevance of parameters. However, there is no method which allows expressing the non-linearity intuitively without requiring knowledge of Bayesian statistics. We propose expert knowledge elicitation through drawing where the expert draws the fit through data points. We then combine the observed data and drawing data to select the right kernel for a Gaussian process. We also conduct a user study for testing the usability of the proposed method. We obtain better performance with the proposed model for kernel selection and extrapolation in comparison to the baseline model using only observed data.Description
Supervisor
Kaski, SamuelThesis advisor
Peltola, TomiKeywords
expert knowledge elicitation, Gaussian process, drawing, kernel selection
