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Mislabel Identification Using Transfer Learning-based Ensemble Method.

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Mislabel Identification Using Transfer Learning-based Ensemble Method.

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Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

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Mislabel Identification Using Transfer Learning-Based Ensemble Method.

Md Shariful Islam¹, Min Jun Kim², Prashanta Dutta¹

¹School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164, USA.

IEEE Access : Practical Innovations, Open Solutions

|May 11, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a new ensemble method using transfer learning to accurately identify and correct mislabeled data in machine learning. The approach significantly improves data labeling accuracy for sensitive applications like virus classification and medical diagnostics.

Keywords:

Adeno-associated virus consensus filtering ensemble model machine learning majority filtering mislabels

Related Experiment Videos

Area of Science:

Machine Learning
Data Science
Bioinformatics

Background:

Accurate data labeling is crucial for supervised machine learning, yet it is labor-intensive and prone to errors.
Existing datasets, including MNIST and ImageNet, contain mislabeled samples, impacting model reliability.
Sensitive applications like medical diagnostics and virus classification demand high data integrity.

Purpose of the Study:

To develop and validate a robust method for identifying and correcting mislabeled training data.
To enhance the reliability of machine learning models in critical applications.
To establish a superior framework for mislabel detection in complex datasets.

Main Methods:

A transfer learning-based ensemble method employing majority and consensus filtering.
Utilized fine-tuned deep neural networks: ResNet-50, ResNet-101, VGG-16, EfficientNet, MobileNet, and Inception.
Validated on MNIST, synthetically corrupted datasets, and adeno-associated virus (AAV) nanopore data.

Main Results:

The ensemble method detected approximately 751 label inconsistencies in the MNIST dataset.
Achieved up to 100% recovery of synthetically injected mislabels using voting strategies.
Successfully identified most mislabeled samples in AAV nanopore datasets, with perfect detection on balanced subsets.

Conclusions:

The proposed ensemble method demonstrates superior accuracy, stability, and true-label recovery compared to existing techniques.
Establishes a strong framework for mislabel detection, particularly for complex, fine-grained data like nanopore signals.
Offers a reliable solution for improving data quality in machine learning pipelines.