Elicitation of Non-Linearity from Expert Drawing

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.