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Updated: Nov 1, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Learning to reweight examples in multi-label classification.

Yongjian Zhong¹, Bo Du¹, Chang Xu²

¹School of Computer Science and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China; National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China; Institute of Artificial Intelligence, Wuhan University, Wuhan, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 19, 2021

Summary

This summary is machine-generated.

This study introduces a novel example reweighting method for multi-label classification. It accounts for instance complexity beyond just loss, improving model accuracy on complex datasets.

Keywords:

Multi-label classification Reweight instance Self-paced learning

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Area of Science:

Machine Learning
Computer Science

Background:

Multi-label classification assigns multiple labels to data instances.
Existing self-paced learning methods for multi-label classification rely solely on loss values for example weighting.
This approach overlooks the unique characteristics and complexities inherent in multi-label data, leading to potential inaccuracies.

Purpose of the Study:

To propose a new example reweighting method for multi-label classification problems.
To address the limitations of existing methods that neglect instance complexities.
To enhance the accuracy and effectiveness of multi-label classification models.

Main Methods:

Developed a novel weighting function that incorporates instance complexity.
Defined instance complexity using the distances between instance features and their corresponding labels.
Ensured the distance metric is optimizable during the training process.

Main Results:

Experimental results on real-world datasets validate the proposed method.
Demonstrated the importance of considering both dynamic and static complexities of multi-label examples.
Showcased the advantages of the new reweighting algorithm over traditional approaches.

Conclusions:

The proposed example reweighting algorithm offers significant advantages for multi-label classification.
Incorporating instance complexity provides a more nuanced and accurate weighting strategy.
This method enhances the performance of multi-label classification by better handling diverse data characteristics.