# Robust Probabilistic Calibration

@inproceedings{Rping2006RobustPC, title={Robust Probabilistic Calibration}, author={Stefan R{\"u}ping}, booktitle={ECML}, year={2006} }

Probabilistic calibration is the task of producing reliable estimates of the conditional class probability P(class | observation) from the outputs of numerical classifiers. A recent comparative study [1] revealed that Isotonic Regression [2] and Platt Calibration [3] are most effective probabilistic calibration technique for a wide range of classifiers. This paper will demonstrate that these methods are sensitive to outliers in the data. An improved calibration method will be introduced that…

## 16 Citations

### Reliable Calibrated Probability Estimation in Classification

- Computer Science
- 2012

This paper proposes an improvement of the calibration with isotonic regression and binning method by using bootstrapping technique, named bootisotonic regressions and boot-binning, respectively, and shows that the new method outperforms the basic isotonic regressors and binners methods in most configurations.

### Probability Calibration Trees

- Computer ScienceACML
- 2017

This work proposes probability calibration trees, a modification of logistic model trees that identifies regions of the input space in which different probability calibration models are learned to improve performance.

### Obtaining Accurate Probabilities Using Classifier Calibration.

- Computer Science
- 2016

A suite of parametric and non-parametric methods for calibrating the output of classification and prediction models and a novel framework to derive calibrated probabilities of causal relationships from observational data that improves the precision and recall of edge predictions.

### Probabilistic Novelty Detection With Support Vector Machines

- Computer ScienceIEEE Transactions on Reliability
- 2014

The development of a Probabilistic calibration technique for one-class SVMs, such that on-line novelty detection may be performed in a probabilistic manner, and the demonstration of the advantages of the proposed method (in comparison to the conventional one- class SVM methodology) using case studies.

### Perplexed Bayes Classifier

- Computer ScienceICON
- 2015

A modification to the Naive Bayes classification algorithm is proposed which improves the classifier’s posterior probability estimates without affecting its performance, and the resulting classifier is called the Perplexed Bayes classifier.

### Threshold Choice Methods: the Missing Link

- Computer ScienceArXiv
- 2011

The analysis provides a comprehensive view of performance metrics as well as a systematic approach to loss minimisation, and derives several connections between the aforementioned performance metrics, and highlights the role of calibration in choosing the threshold choice method.

### Confidence-Based Feature Acquisition to Minimize Training and Test Costs

- Computer ScienceSDM
- 2010

This work presents Confidence-based Feature Acquisition, a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test time, and finds that CFA’s accuracy is at least as high as the other methods, while incurring significantly lower feature acquisition costs.

### Pruning of Rules and Rule Sets

- Computer Science
- 2012

Prepruning and post-pruning are two standard techniques for avoiding overfitting in learning algorithms, which deals with it during learning, while post- pruning addresses this problem after an overfitting rule set has been learned.

### A unified view of performance metrics: translating threshold choice into expected classification loss

- Computer ScienceJ. Mach. Learn. Res.
- 2012

This analysis provides a comprehensive view of performance metrics as well as a systematic approach to loss minimisation which can be summarised as follows: given a model, apply the threshold choice methods that correspond with the available information about the operating condition, and compare their expected losses.

### Tree-structured multiclass probability estimators

- Computer Science
- 2019

It is observed that nested dichotomies systematically produce under-confident predictions, even if the binary classifiers are well calibrated, and especially when the number of classes is high, which means substantial performance gains can be made when probability calibration methods are also applied to the internal models.

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