Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
This work addresses class imbalance in machine learning, particularly for BCI applications like motor imagery, driver fatigue, and migraine phase classification, but it is incremental as it builds on prior ASSOM-based methods.
The authors tackled class imbalance by extending an earlier oversampling method based on Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM), applying it to benchmark datasets and three Brain Computer Interface (BCI) applications, and demonstrating its effectiveness in handling imbalance classification problems.
Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes application to three Brain Computer Interface (BCI) applications. First we compare the performance of our method using some benchmark data sets with several state-of-the-art methods. Finally, we apply the ASSOM-based technique to analyze the three BCI based applications using electroencephalogram (EEG) datasets. These tasks are classification of motor imagery , drivers' fatigue states, and phases of migraine. Our results demonstrate the effectiveness of the ASSOM-based meth od in dealing with imbalance classification problem.