Active Class Selection for Arousal Classification

Active class selection (ACS) studies how to optimally select the classes to obtain training examples so that a good classifier can be constructed from a small number of training examples. It is very useful in situations where the class labels need to be determined before the training examples and features can be obtained. For example, in many emotion classification problems, the emotion (class label) needs to be specified before the corresponding responses can be generated and recorded. However, there has been very limited research on ACS, and to the best knowledge of the authors, ACS has not been introduced to the affective computing community. In this paper, we compare two ACS approaches in an arousal classification application. Experimental results using a kNN classifier show that one of them almost always results in higher classification accuracy than a uniform sampling approach. We expect that ACS, together with transfer learning, will greatly reduce the data acquisition effort to customize an affective computing system.

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