arxiv_ml 90% Match Research Paper Machine learning engineers,Data scientists,AI researchers focused on robustness,Industry practitioners 20 hours ago

Calibration improves detection of mislabeled examples

ai-safety › robustness

📄 Abstract

Abstract: Mislabeled data is a pervasive issue that undermines the performance of machine learning systems in real-world applications. An effective approach to mitigate this problem is to detect mislabeled instances and subject them to special treatment, such as filtering or relabeling. Automatic mislabeling detection methods typically rely on training a base machine learning model and then probing it for each instance to obtain a trust score that each provided label is genuine or incorrect. The properties of this base model are thus of paramount importance. In this paper, we investigate the impact of calibrating this model. Our empirical results show that using calibration methods improves the accuracy and robustness of mislabeled instance detection, providing a practical and effective solution for industrial applications.

Key Contributions

Investigates the impact of model calibration on detecting mislabeled data instances. The paper shows empirically that using calibration methods significantly improves the accuracy and robustness of mislabeled instance detection, providing a practical and effective solution for industrial applications where data quality is crucial.

Business Value

Enhances the reliability and performance of machine learning systems by enabling the detection and handling of mislabeled data. This leads to more trustworthy AI applications and better decision-making in industries.

Paper Metadata

Innovation Type

Methodology Improvement

Deployment Feasibility

Highly feasible, as calibration is a well-understood technique, and the proposed method involves probing trained models. It's directly applicable to existing ML pipelines.

Limitations Addressed

The pervasive issue of mislabeled data in real-world machine learning applications, which degrades model performance. It addresses the need for effective automatic detection of such instances.

Performance Gains

improves the accuracy and robustness of mislabeled instance detection

Technical Tags

mislabeled datacalibrationdetectionmachine learningrobustnesstrust scoreindustrial applicationsdata qualitymodel probingclassification

Research Topics

AI SafetyData QualityMachine Learning RobustnessModel EvaluationReliable AI

Methods & Architectures

model calibrationprobing base modelstrust score calculation

Applications & Tasks

Machine Learning Data Science Industrial Applications Mislabeled DataUndermining Model PerformanceDetecting Incorrect Labels Mislabeled Instance DetectionImproving Model RobustnessData Cleaning

Related Fields

Data MiningQuality AssuranceMachine Learning Operations (MLOps)

Keywords

mislabeled datacalibrationdetectionmachine learningrobustnessdata qualitytrust scoreindustrial AImodel probingreliable AIdata cleaningclassificationperformanceaccuracyreal-world applications

Academic Context

#AI Safety#Data Quality#Machine Learning Robustness#Model Evaluation#Reliable AI

Commercial Potential

Potential Products

Data quality assessment toolMislabeled data detection serviceRobustness enhancement module for ML pipelines

Target Industries

All industries using MLFinanceHealthcareE-commerceManufacturing

Use Case Examples

Identifying and correcting errors in training datasetsImproving the reliability of models used in critical applicationsAutomating data validation processes in ML workflows

Competitive Edge

Offers a practical and effective method to improve mislabeled data detection by leveraging model calibration, outperforming methods that do not consider calibration.

Market Opportunity

Large market for AI quality assurance and data validation tools.

Revenue Models

Integration into ML platformsconsulting services.

Resource Requirements

Compute Needs

Minimal additional compute required beyond the training of the base ML model.

Data Requirements

Requires labeled datasets for training the base model and for evaluating the detection method.

Deployment Constraints

Effectiveness depends on the quality of the base model and the calibration method used.

Scalability

The detection process scales linearly with the number of instances to be checked.

Regulatory Considerations

Ensuring fairness and avoiding bias in mislabel detectionespecially in sensitive applications.

Production Readiness

Maturity Level

Applied Research

Time to Market

3-6 months for integration into existing ML pipelines.

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