Neural network model for imprecise regression with interval dependent variables. (April 2023)

• Record Type: Journal Article
• Authors: Tretiak, Krasymyr; Schollmeyer, Georg; Ferson, Scott
• Is Part Of: Neural networks. Volume 161(2023)
• Page Start: 550
• Page End: 564
• Publication Date: 2023-04
• Subjects: Imprecise regression -- Interval data -- Neural network -- Uncertainty; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.02.005
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper presents a computationally feasible method to compute rigorous bounds on the interval-generalization 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. An additional extension to a multi-layer neural network is also presented. We consider the explanatory variables to be precise point values, but the measured dependent values are characterized by interval bounds without any probabilistic information. The proposed iterative method estimates the lower and upper bounds of the expectation region, which is an envelope of all possible precise regression lines obtained by ordinary regression analysis based on any configuration of real-valued points from the respective y -intervals and their x -values.