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- Tuve Löfström, Jing Zhao, Henrik Linusson, Karl Jansson
- SCAI
- 2015

- Ulf Johansson, Henrik Boström, Tuve Löfström, Henrik Linusson
- Machine Learning
- 2014

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 most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of… (More)

- Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Johansson
- COPA
- 2016

- Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström
- AIAI Workshops
- 2014

In the conformal prediction literature, it appears axiomatic that transductive conformal classifiers possess a higher predictive efficiency than inductive conformal classifiers, however, this depends on whether or not the nonconformity function tends to overfit misclassified test examples. With the conformal prediction framework’s increasing popularity, it… (More)

- Tuve Löfström, Henrik Boström, Henrik Linusson, Ulf Johansson
- Intell. Data Anal.
- 2015

- Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Johansson
- Annals of Mathematics and Artificial Intelligence
- 2017

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 framework requires a real-valued function, called nonconformity measure, to be specified. The nonconformity measure does not affect the error rate, but the resulting… (More)

- Ulf Johansson, Cecilia Sönströd, Henrik Linusson
- 2015 International Joint Conference on Neural…
- 2015

Conformal predictors use machine learning models to output prediction sets. For regression, a prediction set is simply a prediction interval. All conformal predictors are valid, meaning that the error rate on novel data is bounded by a preset significance level. The key performance metric for conformal predictors is their efficiency, i.e., the size of the… (More)

- Henrik Linusson, Ulf Johansson, Tuve Löfström
- PAKDD
- 2014

This paper suggests a modification of the Conformal Prediction framework for regression that will strengthen the associated guarantee of validity. We motivate the need for this modification and argue that our conformal regressors are more closely tied to the actual error distribution of the underlying model, thus allowing for more natural interpretations of… (More)

- Ulf Johansson, Rikard König, Henrik Linusson, Tuve Löfström, Henrik Boström
- AIAI Workshops
- 2014

This paper extends the conformal prediction framework to rule extraction, making it possible to extract interpretable models from opaque models in a setting where either the infidelity or the error rate is bounded by a predefined significance level. Experimental results on 27 publicly available data sets show that all three setups evaluated produced valid… (More)