Non-linear shrinking of linear model errors.

Autor: Helin R; Norwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway. Electronic address: runar.helin@nmbu.no., Indahl U; Norwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway., Tomic O; Norwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway., Liland KH; Norwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway.
Jazyk: angličtina
Zdroj: Analytica chimica acta [Anal Chim Acta] 2023 Jun 01; Vol. 1258, pp. 341147. Date of Electronic Publication: 2023 Apr 04.
DOI: 10.1016/j.aca.2023.341147
Abstrakt: Background: Artificial neural networks (ANNs) can be a powerful tool for spectroscopic data analysis. Their ability to detect and model complex relations in the data may lead to outstanding predictive capabilities, but the predictions themselves are difficult to interpret due to the lack of understanding of the black box ANN models. ANNs and linear methods can be combined by first fitting a linear model to the data followed by a non-linear fitting of the linear model residuals using an ANN. This paper explores the use of residual modelling in high-dimensional data using modern neural network architectures.
Results: By combining linear- and ANN modelling, we demonstrate that it is possible to achieve both good model performance while retaining interpretations from the linear part of the model. The proposed residual modelling approach is evaluated on four high-dimensional datasets, representing two regression and two classification problems. Additionally, a demonstration of possible interpretation techniques are included for all datasets. The study concludes that if the modelling problem contains sufficiently complex data (i.e., non-linearities), the residual modelling can in fact improve the performance of a linear model and achieve similar performance as pure ANN models while retaining valuable interpretations for a large proportion of the variance accounted for.
Significance and Novelty: The paper presents a residual modelling scheme using modern neural network architectures. Furthermore, two novel extensions of residual modelling for classification tasks are proposed. The study is seen as a step towards explainable AI, with the aim of making data modelling using artificial neural networks more transparent.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: