COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Information-based objective functions for active data selection
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Primitives-based evaluation and estimation of emotions in speech
Speech Communication
ECML '07 Proceedings of the 18th European conference on Machine Learning
Fundamentals of physiological computing
Interacting with Computers
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Active learning with multiple views
Journal of Artificial Intelligence Research
Incorporating diversity and density in active learning for relevance feedback
ECIR'07 Proceedings of the 29th European conference on IR research
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Affective Computing
Inductive transfer learning for handling individual differences in affective computing
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Inductive transfer learning for handling individual differences in affective computing
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
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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.