Academic

Label Shift Estimation With Incremental Prior Update

arXiv:2604.01651v1 Announce Type: new Abstract: An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over time and across locations; fraud detection models must adapt as patterns of fraudulent activity shift; the category distribution of social media posts changes based on trending topics and user demographics. In the task of label shift estimation, the goal is to estimate the changing label distribution $p_t(y)$ in the testing set, assuming the likelihood $p(x|y)$ does not change, implying no concept drift. In this paper, we propose a new approach for post-hoc label shift estimation, unlike previous methods that perform moment matching with confusion matrix estimated from a validation set or maximize the likelihood of the new data with an expectation-maximization algorithm. We aim to incrementally

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Yunrui Zhang, Gustavo Batista, Salil S. Kanhere · · 1 min read · 2 views
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arXiv:2604.01651v1 Announce Type: new Abstract: An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over time and across locations; fraud detection models must adapt as patterns of fraudulent activity shift; the category distribution of social media posts changes based on trending topics and user demographics. In the task of label shift estimation, the goal is to estimate the changing label distribution $p_t(y)$ in the testing set, assuming the likelihood $p(x|y)$ does not change, implying no concept drift. In this paper, we propose a new approach for post-hoc label shift estimation, unlike previous methods that perform moment matching with confusion matrix estimated from a validation set or maximize the likelihood of the new data with an expectation-maximization algorithm. We aim to incrementally update the prior on each sample, adjusting each posterior for more accurate label shift estimation. The proposed method is based on intuitive assumptions on classifiers that are generally true for modern probabilistic classifiers. The proposed method relies on a weaker notion of calibration compared to other methods. As a post-hoc approach for label shift estimation, the proposed method is versatile and can be applied to any black-box probabilistic classifier. Experiments on CIFAR-10 and MNIST show that the proposed method consistently outperforms the current state-of-the-art maximum likelihood-based methods under different calibrations and varying intensities of label shift.

Executive Summary

This article proposes a novel approach to label shift estimation in supervised learning, aiming to incrementally update the prior on each sample to achieve more accurate label shift estimation. The proposed method relies on a weaker notion of calibration compared to existing methods and is applicable to any black-box probabilistic classifier. Experiments on CIFAR-10 and MNIST demonstrate the superiority of the proposed method over the current state-of-the-art maximum likelihood-based methods under various calibration and label shift intensity conditions.

Key Points

  • ▸ The proposed method incrementally updates the prior on each sample for label shift estimation.
  • ▸ The method relies on a weaker notion of calibration compared to existing methods.
  • ▸ The approach is applicable to any black-box probabilistic classifier.

Merits

Strength in Addressing Real-World Scenarios

The proposed method tackles real-life scenarios where label distributions change over time, such as medical diagnosis, fraud detection, and social media posts, making it a valuable contribution to the field.

Improved Calibration and Flexibility

The method's reliance on a weaker notion of calibration and its applicability to any black-box probabilistic classifier enhance its flexibility and practicality.

State-of-the-Art Performance

Experiments on CIFAR-10 and MNIST demonstrate the proposed method's ability to outperform existing maximum likelihood-based methods, solidifying its position as a state-of-the-art solution.

Demerits

Potential Overreliance on Prior Updates

The method's reliance on incremental prior updates may lead to overfitting or instability, particularly when dealing with complex or rapidly changing label distributions.

Limited Generalizability to Non-Probabilistic Classifiers

The proposed method's applicability is restricted to black-box probabilistic classifiers, which may limit its generalizability to other types of classifiers.

Expert Commentary

The proposed method is a significant contribution to the field of label shift estimation, offering a novel and effective approach to adapting to changing label distributions. However, its potential limitations and overreliance on prior updates warrant further investigation and exploration of alternative methods. Nevertheless, the method's state-of-the-art performance and flexibility make it a valuable tool for practitioners and researchers alike.

Recommendations

  • ✓ Future research should focus on addressing the potential limitations of the proposed method, such as overfitting and instability, and exploring alternative approaches to label shift estimation.
  • ✓ The method's applicability to real-world scenarios should be further evaluated and demonstrated through case studies and practical applications.

Sources

Original: arXiv - cs.LG

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