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Learn how clustering techniques can enhance the precision and certainty of prediction intervals in neural networks, enabling better decision-making. Explore a detailed approach and results using synthetic data sets for estimation.
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Using Clustering to Make Prediction Intervals For Neural Networks Claus Benjaminsen ECE539 - final project fall 2005
What is a prediction interval? • An interval within which the true target value is predicted to be • Prediction intervals are often defined by • Interval end values and an associated probability • A Gaussian distribution with a certain mean and variance
Motivation • Give user a more informative output • Precision of prediction (width of interval) • Max and min limits (end values of interval) • Certainty of prediction (associated probability) • Make user able to make better decisions
Approach 1 • Use clustering to group training feature vectors • Associate the mean training error of all the features within each cluster to the corresponding cluster center • Estimate prediction interval for all training features in a cluster by scaling the associated error
Approach 2 • Given a new input its membership to one of the cluster centers is determined • The prediction interval for the new input is then the same as for the training data belonging to that cluster
Data set used • Synthetic data set • 1 input feature • 1 output target • 256 samples • Divided into: • 156 training samples • 100 testing samples
Results • Plot of the test data along with prediction intervals estimated by the clustering method
Results 2 • The cost function is calculated as the mean squared distance from the edge of the prediction interval to the test target for all targets not inside prediction intervals • The clustering method has the best performance of the three different methods
Discussion • Model can become big, when input feature space is big and the number of training samples is high • Problems when new inputs doesn’t “look” like any of the training inputs
Conclusion • The clustering method can estimate prediction intervals, which yields a very good performance • Data used is very well suited for clustering method – might not show same good result for other types of datasets • This will have to be tested in the future!
