Ozone day prediction with radial basis function networks

Authors:
Hyontai Sug
Affiliations:
Division of Computer and Information Engineering, Dongseo University, Busan, Republic of Korea
Venue:
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II
Year:
2010

Citing 14
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
A comparison between neural networks and decision trees based on data from industrial radiographic testing

Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Radial basis function networks 1: recent developments in theory and applications

Radial basis function networks 1: recent developments in theory and applications
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning

Data Mining and Knowledge Discovery
SLIQ: A Fast Scalable Classifier for Data Mining

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Is random model better? On its accuracy and efficiency

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Data Mining Methods and Models

Data Mining Methods and Models
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Data Mining

Data Mining
Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond

Knowledge and Information Systems
Introduction to Neural Networks for C#, 2nd Edition

Introduction to Neural Networks for C#, 2nd Edition

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is not known to decide a proper number of clusters for clustering, so is true in the task of deciding proper number of clusters for RBF networks that are based on clustering algorithms, so that the number of clusters in RBF networks tend to be arbitrary. In RBF networks as the number of clusters changes, the accuracy of the trained RBF networks changes also. So this paper suggests a progressive approach to find a proper number of clusters to find good RBF networks with respect to accuracy especially for ozone day prediction data. Experiments with the data set showed better results than random forest method.