Neural networks regression inductive conformal predictor and its application to total electron content prediction

Authors:
Harris Papadopoulos;Haris Haralambous
Affiliations:
Computer Science and Engineering Department, Frederick University, Palouriotisa, Nicosia, Cyprus;Computer Science and Engineering Department, Frederick University, Palouriotisa, Nicosia, Cyprus
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

Citing 12
Cited 0

Transductive Confidence Machines for Pattern Recognition

ECML '02 Proceedings of the 13th European Conference on Machine Learning
Inductive Confidence Machines for Regression

ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ridge Regression Confidence Machine

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Algorithmic Learning in a Random World

Algorithmic Learning in a Random World
An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds

Environmental Modelling & Software
Application of fuzzy T-norms towards a new Artificial Neural Networks' evaluation framework: A case from wood industry

Information Sciences: an International Journal
A non-symbolic implementation of abdominal pain estimation in childhood

Information Sciences: an International Journal
Radar target recognition using contourlet packet transform and neural network approach

Signal Processing
Serum Proteomic Abnormality Predating Screen Detection of Ovarian Cancer

The Computer Journal
Transduction with confidence and credibility

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Normalized nonconformity measures for regression Conformal Prediction

AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Learning by transduction

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we extend regression Neural Networks (NNs) based on the Conformal Prediction (CP) framework for accompanying predictions with reliable measures of confidence. We follow a modification of the original CP approach, called Inductive Conformal Prediction (ICP), which enables us to overcome the computational inefficiency problem of CP. Unlike the point predictions produced by conventional regression NNs the proposed approach produces predictive intervals that satisfy a given confidence level. We apply it to the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links. Our experimental results on a dataset collected over a period of 11 years show that the resulting predictive intervals are both well-calibrated and tight enough to be useful in practice.