Selective Probabilistic Classifier Based on Hypothesis Testing

• URL: http://arxiv.org/abs/2105.03876v2
• Date: Tue, 11 May 2021 20:41:58 GMT
• Title: Selective Probabilistic Classifier Based on Hypothesis Testing
• Authors: Saeed Bakhshi Germi and Esa Rahtu and Heikki Huttunen
• Abstract summary: We propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. It is shown that the proposed method can achieve a broader range of operation and cover a lower False Positive Ratio than the alternative.
• Score: 14.695979686066066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. Previous works tend to apply a threshold either on the classification scores or the loss function to reject the inputs that violate the assumption. However, these methods cannot achieve the low False Positive Ratio (FPR) required in safety applications. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. With probabilistic networks, it is possible to estimate the distribution of outcomes instead of a single output. By utilizing Z-test over the mean and standard deviation for each class, the proposed method can estimate the statistical significance of the network certainty and reject uncertain outputs. The proposed method was experimented on with different configurations of the COCO and CIFAR datasets. The performance of the proposed method is compared with the Softmax Response, which is a known top-performing method. It is shown that the proposed method can achieve a broader range of operation and cover a lower FPR than the alternative.

Related papers

• Meta-learning for Positive-unlabeled Classification [40.11462237689747]
  The proposed method minimizes the test classification risk after the model is adapted to PU data. The method embeds each instance into a task-specific space using neural networks. We empirically show that the proposed method outperforms existing methods with one synthetic and three real-world datasets.
  arXiv  Detail & Related papers  (2024-06-06T01:50:01Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability [50.44439018155837]
  We propose to include a calibration term directly into the training objective of the neural model. By introducing a relaxation of the classical formulation of calibration error we enable end-to-end backpropagation. It is directly applicable to existing computational pipelines allowing reliable black-box posterior inference.
  arXiv  Detail & Related papers  (2023-10-20T10:20:45Z)
• Risk Consistent Multi-Class Learning from Label Proportions [64.0125322353281]
  This study addresses a multiclass learning from label proportions (MCLLP) setting in which training instances are provided in bags. Most existing MCLLP methods impose bag-wise constraints on the prediction of instances or assign them pseudo-labels. A risk-consistent method is proposed for instance classification using the empirical risk minimization framework.
  arXiv  Detail & Related papers  (2022-03-24T03:49:04Z)
• Kernel Robust Hypothesis Testing [20.78285964841612]
  In this paper, uncertainty sets are constructed in a data-driven manner using kernel method. The goal is to design a test that performs well under the worst-case distributions over the uncertainty sets. For the Neyman-Pearson setting, the goal is to minimize the worst-case probability of miss detection subject to a constraint on the worst-case probability of false alarm.
  arXiv  Detail & Related papers  (2022-03-23T23:59:03Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Optimal strategies for reject option classifiers [0.0]
  In classification with a reject option, the classifier is allowed in uncertain cases to abstain from prediction. We coin a symmetric definition, the bounded-coverage model, which seeks for a classifier with minimal selective risk and guaranteed coverage. We propose two algorithms to learn the proper uncertainty score from examples for an arbitrary black-box classifier.
  arXiv  Detail & Related papers  (2021-01-29T11:09:32Z)
• Selective Classification via One-Sided Prediction [54.05407231648068]
  One-sided prediction (OSP) based relaxation yields an SC scheme that attains near-optimal coverage in the practically relevant high target accuracy regime. We theoretically derive bounds generalization for SC and OSP, and empirically we show that our scheme strongly outperforms state of the art methods in coverage at small error levels.
  arXiv  Detail & Related papers  (2020-10-15T16:14:27Z)
• Regression with reject option and application to kNN [0.0]
  We refer to this framework as regression with reject option as an extension of classification with reject option. We provide a semi-supervised estimation procedure of the optimal rule involving two datasets. The resulting predictor with reject option is shown to be almost as good as the optimal predictor with reject option both in terms of risk and rejection rate.
  arXiv  Detail & Related papers  (2020-06-30T08:20:57Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.