A Batch-mode Active Learning Method Based on the Nearest Average-class Distance (NACD) for Multiclass Brain-Computer Interfaces (Journal of Fiber Bioengineering and Informatics, 2014)
In this paper, a novel batch-mode active learning method based on the nearest average-class distance(ALNACD) is proposed to solve multi-class problems with Linear Discriminate Analysis (LDA) classifiers.Using the Nearest Average-class Distance (NACD) query function, the ALNACD algorithm selectsa batch of most uncertain samples from unlabeled data to improve gradually pre-trained classifiers'performance. As our method only needs a small set of labeled samples to train initial classifiers, it isvery useful in applications like Brain-computer Interface (BCI) design. To verify the effectiveness of theproposed ALNACD method, we test the ALNACD algorithm on the Dataset 2a of BCI Competition IV.The test results show that the ALNACD algorithm offers similar classification results using less samplelabeling effort than Random Sampling (RS) method. It also provides competitive results compared withactive Support Vector Machine (active SVM), but uses less time than the active SVM in terms of thetraining.
Keywords: Active Learning; Linear Discriminant Analysis (LDA); Nearest Average-class Distance (NACD); Brain-computer Interface (BCI)