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Regression conformal prediction with random forests
Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The mostExpand
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On the Calibration of Aggregated Conformal Predictors
Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed oExpand
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Evolved decision trees as conformal predictors
In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class isExpand
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Accelerating difficulty estimation for conformal regression forests
The conformal prediction framework allows for specifying the probability of making incorrect predictions by a user-provided confidence level. In addition to a learning algorithm, the frameworkExpand
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Effective utilization of data in inductive conformal prediction using ensembles of neural networks
Conformal prediction is a new framework producing region predictions with a guaranteed error rate. Inductive conformal prediction (ICP) was designed to significantly reduce the computational costExpand
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Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers
In the conformal prediction literature, it appears axiomatic that transductive conformal classifiers possess a higher predictive efficiency than inductive conformal classifiers, however, this dependsExpand
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The Importance of Diversity in Neural Network Ensembles - An Empirical Investigation
When designing ensembles, it is almost an axiom that the base classifiers must be diverse in order for the ensemble to generalize well. Unfortunately, there is no clear definition of the key termExpand
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Interpretable regression trees using conformal prediction
A key property of conformal predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset level of confidence. For regression, this is achieved by turning the pointExpand
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Model-agnostic nonconformity functions for conformal classification
A conformai predictor outputs prediction regions, for classification label sets. The key property of all conformai predictors is that they are valid, i.e., their error rate on novel data is boundedExpand
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Overproduce-and-select: The grim reality
Overproduce-and-select (OPAS) is a frequently used paradigm for building ensembles. In static OPAS, a large number of base classifiers are trained, before a subset of the available models is selectedExpand
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