A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and the bias/variance dilemma
Neural Computation
Artificial Intelligence Review - Special issue on lazy learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
2006 Special issue: Machine learning in sedimentation modelling
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2006 Special issue: Modular learning models in forecasting natural phenomena
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
A comparison of some error estimates for neural network models
Neural Computation
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
Prediction limit estimation for neural network models
IEEE Transactions on Neural Networks
Using radial basis functions to approximate a function and its error bounds
IEEE Transactions on Neural Networks
Mining for the most certain predictions from dyadic data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Artificial Intelligence in Medicine
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
ANNs and Other Machine Learning Techniques in Modelling Models' Uncertainty
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Constructing prediction intervals for neural network metamodels of complex systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fuzzy neural networks for water level and discharge forecasting with uncertainty
Environmental Modelling & Software
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
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A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well. oposed as well.