Classifier Transfer with Data Selection Strategies for Online Support Vector Machine Classification with Class Imbalance
In Journal of Neural Engineering, IOP Publishing, volume 14, number 2, pages 025003, Feb/2017.
Objective: Classifier transfers usually come with dataset shifts. To overcome
dataset shifts in practical applications, we consider the limitations in computational resources
in this paper for the adaptation of batch learning algorithms, like the support vector machine
(SVM). Approach: We focus on data selection strategies which limit the size of the stored
training data by different inclusion, exclusion, and further dataset manipulation criteria like
handling class imbalance with two new approaches. We provide a comparison of the strategies
with linear SVMs on several synthetic datasets with different data shifts as well as on different
transfer settings with electroencephalographic (EEG) data. Main Results: For the synthetic
data, 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 samples near the decision boundary of the SVM
which reduces processing resources. Significance: For brain-computer interfaces based on
EEG data, models trained on data from a calibration session, a previous recording session, or
even from a recording session with another subject are used. We show, that by using the right
combination of data selection criteria, it is possible to adapt the SVM classifier to overcome
the performance drop from the transfer.
Support Vector Machine, Online Learning, Brain-Computer Interface, Electroencephalogram, Incremental/Decremental Learning, P300, Movement Prediction