Cross-Validation and Uncertainty Determination for Randomized Neural Networks with Applications to Mobile Sensors

TL;DR

This paper explores cross-validation and uncertainty quantification for randomized neural networks, focusing on their application in mobile sensors and addressing challenges with non-stationary, dependent data samples.

Contribution

It introduces a cross-validation method for randomized neural networks and a two-stage estimation approach for confidence intervals of out-of-sample errors, tailored for mobile sensor data.

Findings

Effective cross-validation for randomized networks under non-stationary data

A computational method for confidence intervals of prediction errors

Application to vehicle photovoltaics prediction problem

Abstract

Randomized artificial neural networks such as extreme learning machines provide an attractive and efficient method for supervised learning under limited computing ressources and green machine learning. This especially applies when equipping mobile devices (sensors) with weak artificial intelligence. Results are discussed about supervised learning with such networks and regression methods in terms of consistency and bounds for the generalization and prediction error. Especially, some recent results are reviewed addressing learning with data sampled by moving sensors leading to non-stationary and dependent samples. As randomized networks lead to random out-of-sample performance measures, we study a cross-validation approach to handle the randomness and make use of it to improve out-of-sample performance. Additionally, a computationally efficient approach to determine the resulting uncertainty in terms of a confidence interval for the mean out-of-sample prediction error is discussed based on two-stage estimation. The approach is applied to a prediction problem arising in vehicle integrated photovoltaics.