![[2602.20947] Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20947] Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation
Summary
This article presents a novel method for estimating confidence bounds in binary classification using Wilson Score Kernel Density Estimation, demonstrating its effectiveness and efficiency compared to traditional methods.
Why It Matters
As deep learning-based binary classifiers gain traction in critical applications, reliable confidence bounds are essential for ensuring system performance. This research offers a new approach that balances accuracy and computational efficiency, which is crucial for industries relying on automated decision-making.
Key Takeaways
- Introduces Wilson Score Kernel Density Estimation for binary classification.
- Provides reliable confidence bounds essential for critical applications.
- Demonstrates performance comparable to Gaussian Process Classification with lower complexity.
- Evaluated across four datasets, showcasing versatility in feature extraction.
- Addresses the need for statistical significance in automated systems.
Computer Science > Machine Learning arXiv:2602.20947 (cs) [Submitted on 24 Feb 2026] Title:Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation Authors:Thorbjørn Mosekjær Iversen, Zebin Duan, Frederik Hagelskjær View a PDF of the paper titled Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation, by Thorbj{\o}rn Mosekj{\ae}r Iversen and 2 other authors View PDF Abstract:The performance and ease of use of deep learning-based binary classifiers have improved significantly in recent years. This has opened up the potential for automating critical inspection tasks, which have traditionally only been trusted to be done manually. However, the application of binary classifiers in critical operations depends on the estimation of reliable confidence bounds such that system performance can be ensured up to a given statistical significance. We present Wilson Score Kernel Density Classification, which is a novel kernel-based method for estimating confidence bounds in binary classification. The core of our method is the Wilson Score Kernel Density Estimator, which is a function estimator for estimating confidence bounds in Binomial experiments with conditionally varying success probabilities. Our method is evaluated in the context of selective classification on four different datasets, illustrating its use as a classification head of any feature extractor, including vision foundation models....
