Neural network model for imprecise regression with interval dependent variables

Understand

This paper presents a computationally feasible method to compute rigorous bounds on the interval-generalisation of regression analysis to account for epistemic uncertainty in the output variables.

• The new iterative method uses machine learning algorithms to fit an imprecise regression model to data that consist of intervals rather than point values.
• The method is based on a single-layer interval neural network which can be trained to produce an interval prediction.
• It seeks parameters for the optimal model that minimizes the mean squared error between the actual and predicted interval values of the dependent variable using a first-order gradient-based optimization and interval analysis computations to model the measurement imprecision of the data.