Abstract
This work presents a comparison of methods to predict ground-level ozone to highlight differences in the ability of the algorithms and to compare their performance to an established signal to noise-based prediction method. Existing data related to weather conditions and ground-level ozone was divided into a training set and a test set. Three algorithms were trained using the training set to create predictors, which were then analyzed with the test set, and then compared to the Taguchi Method to determine performance. It was found that the newly introduced R-LCS performed well on this problem, predictors using the Taguchi method had a smaller deviation from actual results. This indicates an additional factor other than the level of correlation in the data that dictates how well these predictors perform on classification problems. © 2012 IEEE.
Recommended Citation
B. Daniels et al., "A Comparison of Representations for the Prediction of Ground-level Ozone Concentration," 2012 IEEE Congress on Evolutionary Computation, CEC 2012, article no. 6252876, Institute of Electrical and Electronics Engineers, Oct 2012.
The definitive version is available at https://doi.org/10.1109/CEC.2012.6252876
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
classifier; evolutionary computation; predictor
International Standard Book Number (ISBN)
978-146731509-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
04 Oct 2012
