• Corpus ID: 236772351

Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data

@article{Folgoc2021BayesianAO,
  title={Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data},
  author={Lo{\"i}c Le Folgoc and Vasileios Baltatzis and Amir Alansary and Sujal Desai and Anand Devaraj and Sam Ellis and Octavio Martinez Manzanera and Fahdi Kanavati and Arjun Nair and Julia Anne Schnabel and Ben Glocker},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.00250}
}
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for machine learning models. They cause significant gaps between model performance in the lab and in the real world. Our work is a solution to prevalence bias. Prevalence bias is the discrepancy between the prevalence of a pathology and its sampling rate in the… 

References

SHOWING 1-10 OF 61 REFERENCES
Use and misuse of the receiver operating characteristic curve in risk prediction.
The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are
Adjusting for selection bias in retrospective, case-control studies.
TLDR
The method can help to determine whether selection bias is present and thus confirm the validity of study conclusions when no evidence of selection bias can be found, and is demonstrated using simulations that the estimates of the odds ratios produced by the method are consistently closer to the true odds ratio than standard odds ratio estimates using logistic regression.
Learning and evaluating classifiers under sample selection bias
TLDR
This paper formalizes the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Abstract A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function. This approach is effective in cases such as sample surveys or design
Sample Selection Bias Correction Theory
TLDR
A theoretical analysis of sample selection bias correction based on the novel concept of distributional stability which generalizes the existing concept of point-based stability and can be used to analyze other importance weighting techniques and their effect on accuracy when using a distributionally stable algorithm.
The Foundations of Cost-Sensitive Learning
TLDR
It is argued that changing the balance of negative and positive training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods, and the recommended way of applying one of these methods is to learn a classifier from the training set and then to compute optimal decisions explicitly using the probability estimates given by the classifier.
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
TLDR
This article shows how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario.
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics,
Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation
TLDR
This study analyzes overfitting by examining how the distribution of logits alters in relation to how much the model overfits, and derives asymmetric modifications of existing loss functions and regularizers including a large margin loss, focal loss, adversarial training and mixup which specifically aim at reducing the shift observed when embedding unseen samples of the under-represented class.
A Structural Approach to Selection Bias
TLDR
This work argues that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or acause of the outcome.
...
1
2
3
4
5
...