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Related Experiment Video

Updated: Mar 7, 2026

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

Classifier transfer with data selection strategies for online support vector machine classification with class

Mario Michael Krell¹, Nils Wilshusen, Anett Seeland

¹Robotics Research Group, University of Bremen, Robert-Hooke-Str. 1, Bremen, Germany.

Journal of Neural Engineering

|February 14, 2017

Summary

This summary is machine-generated.

This study introduces data selection strategies to adapt Support Vector Machine (SVM) classifiers despite dataset shifts, considering computational limits. Effective strategies depend on data drift intensity, with some methods outperforming others for specific scenarios.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Machine Learning
Computational Neuroscience

Background:

Dataset shifts pose challenges for classifier transfer learning.
Adapting batch learning algorithms like Support Vector Machines (SVMs) requires efficient strategies under computational constraints.

Purpose of the Study:

To investigate data selection strategies for adapting SVM classifiers to dataset shifts with limited computational resources.
To evaluate the effectiveness of different data inclusion, exclusion, and balancing criteria.

Main Methods:

Focused on data selection strategies including inclusion, exclusion, and class imbalance handling.
Compared strategies using linear SVMs on synthetic datasets with varying data shifts.
Evaluated strategies on electroencephalographic (EEG) data in different transfer learning settings.

Main Results:

For synthetic data, including misclassified samples was highly effective.
For EEG transfer learning, retaining all data and removing the oldest samples performed best with significant drifts.
For minor drifts, adding samples near the SVM decision boundary was sufficient and resource-efficient.

Conclusions:

Data selection strategies can successfully adapt SVM classifiers to overcome performance drops caused by dataset shifts.
The optimal strategy is contingent on the intensity of data drift.
These findings are significant for brain-computer interfaces utilizing EEG data.