Classifier Transfer with Data Selection Strategies for Online Support Vector Machine Classification with Class Imbalance

TL;DR

This paper reviews and compares online SVM strategies for handling dataset shifts, emphasizing data selection methods that improve adaptation efficiency and performance in transfer learning scenarios.

Contribution

It introduces and evaluates various data selection strategies for online SVMs, demonstrating how to optimize classifier adaptation under resource constraints and data drift conditions.

Findings

Misclassified sample addition performs well in synthetic data.

Adding all samples can be worse than selective criteria.

For small drifts, updating only support vectors suffices.

Abstract

Objective: Classifier transfers usually come with dataset shifts. To overcome them, online strategies have to be applied. For practical applications, limitations in the computational resources for the adaptation of batch learning algorithms, like the SVM, have to be considered. Approach: We review and compare several strategies for online learning with SVMs. We focus on data selection strategies which limit the size of the stored training data [...] Main Results: For different data shifts, different criteria are appropriate. For the synthetic data, adding all samples to the pool of considered samples performs often significantly worse than other criteria. Especially, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add potential new support vectors of the SVM which reduces processing resources. Significance: For BCIs based on EEG models, trained on data from a calibration session, a previous recording session, or even from a recording session with one or several other subjects, are used. This transfer of the learned model usually decreases the performance and can therefore benefit from online learning which adapts the classifier like the established SVM. We show that by using the right combination of data selection criteria, it is possible to adapt the classifier and largely increase the performance. Furthermore, in some cases it is possible to speed up the processing and save computational by updating with a subset of special samples and keeping a small subset of samples for training the classifier.