Per-Sample Prediction Intervals for Extreme Learning Machines

Abstract

Prediction Intervals in Supervised Machine Learning Bound the Region Where the True Outputs of New Samples May Fall. They Are Necessary in the Task of Separating Reliable Predictions of a Trained Model from Near Random Guesses, Minimizing the Rate of False Positives, and Other Problem-Specific Tasks in Applied Machine Learning. Many Real Problems Have Heteroscedastic Stochastic Outputs, Which Explains the Need of Input-Dependent Prediction Intervals. This Paper Proposes to Estimate the Input-Dependent Prediction Intervals by a Separate Extreme Learning Machine Model, using Variance of its Predictions as a Correction Term Accounting for the Model Uncertainty. the Variance is Estimated from the Model's Linear Output Layer with a Weighted Jackknife Method. the Methodology is Very Fast, Robust to Heteroscedastic Outputs, and Handles Both Extremely Large Datasets and Insufficient Amount of Training Data.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Confidence interval; Coverage; ELM; False positives; Heteroscedastic; Prediction interval; variance estimation

International Standard Serial Number (ISSN)

1868-808X; 1868-8071

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 May 2019

Link to Full Text

Share

 
COinS